diff --git a/Bindings/Python/tutorials/Tutorial 1 - Introduction to OpenSim.ipynb b/Bindings/Python/tutorials/Tutorial 1 - Introduction to OpenSim.ipynb new file mode 100644 index 0000000000..754603024e --- /dev/null +++ b/Bindings/Python/tutorials/Tutorial 1 - Introduction to OpenSim.ipynb @@ -0,0 +1,387 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YS-0eq5Y6BjU" + }, + "source": [ + "# 1. Introduction to OpenSim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZZClpIjbitD4" + }, + "source": [ + "\n", + "## 1.1. Objectives" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AKANMMa6iycq" + }, + "source": [ + "**Introduction to OpenSim**\n", + "\n", + "[OpenSim](https://opensim.stanford.edu/) is an open-source software that allows users to develop, analyze, and visualize models of the musculoskeletal system, and to generate dynamic simulations of movement [[1]](https://doi.org/10.1109/TBME.2007.901024). OpenSim enables users to create custom studies, including investigating the effects of musculoskeletal geometry, joint kinematics, and muscle-tendon properties on the forces and joint moments that the muscles can produce. With OpenSim, our goal is to provide a framework that allows the biomechanics community to create, share, and extend a library of models and dynamic simulation tools to study and quantify human and animal movement.\n", + "\n", + "**Purpose**\n", + "\n", + "The purpose of this tutorial is to introduce users to the OpenSim [conda package](https://anaconda.org/opensim-org/opensim), that allows the use of the library in [Jupyter notebooks](https://jupyter.org/) and other Python environments. In this tutorial you will:\n", + "\n", + "* Learn to set up OpenSim in a Jupyter notebook using [Google Colab](https://colab.research.google.com/?hl=en).\n", + "* Create a simple pendulum in OpenSim.\n", + "* Visualize the position of the pendulum using [matplotlib](https://matplotlib.org/).\n", + "\n", + "**Format**\n", + "\n", + "This tutorial guides you through the setup of the OpenSim conda package in Google Colab. Then, to test if OpenSim was installed correctly, you will create a simple pendulum, simulate it, and visualize its position. As you complete each section of the tutorial, feel free to explore the OpenSim conda package and modify the code blocks of the tutorial on your own.\n", + "\n", + "**Local Installation**\n", + "\n", + "You may also run this notebook using a locally installed OpenSim `conda` package. Visit [the OpenSim python scripting Confluence page](https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53085346/Scripting+in+Python) for more information on how to install OpenSim in a local Python environment. Then, you may skip to Section 1.3 below." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kPnGlZIT8oiM" + }, + "source": [ + "## 1.2. Set up `conda` and OpenSim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3kxYrs4Hi6Nl" + }, + "source": [ + "In the following cell, you will use [condacolab](https://github.com/conda-incubator/condacolab) to install [Miniconda](https://docs.conda.io/en/latest/miniconda.html). If you want to install a different conda distribution (e.g., Anaconda, Mambaforge) check [condacolab's documentation](https://github.com/conda-incubator/condacolab#usage). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5-sX9L3f6Asq" + }, + "outputs": [], + "source": [ + "!pip install -q condacolab\n", + "import condacolab\n", + "condacolab.install()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LdmpfqC8Dt0x" + }, + "source": [ + "Don't worry if after executing the previous cell you get an error saying that your session has failed. This happens because `condacolab` needs to restart the session for the changes to take effect. Therefore, you have to execute the previous cell before executing any other cell.\n", + "\n", + "Now, execute the following cell to install the OpenSim `conda` package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "H9WE2h3fD61J" + }, + "outputs": [], + "source": [ + "!conda install opensim-org::opensim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hj5Y1HCXEb_M" + }, + "source": [ + "## 1.3. Create a pendulum model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1W4hVOJvEmc5" + }, + "source": [ + "In this section we will create a simple pendulum, simulate it using time-stepping integration, and plot the resulting states trajectory. First, run the following cell to import the OpenSim module. If successful, the current OpenSim version and build date should print to the console." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "H4vWQbmhEjVr" + }, + "outputs": [], + "source": [ + "import opensim as osim\n", + "osim.GetVersionAndDate()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RlqyWYr3HfAD" + }, + "source": [ + "Now, let's create a pendulum model. We will use `ModelFactory`, a utility class that allows you to construct common OpenSim `Model`s. Here, we will create a single-link pendulum using `ModelFactor::createPendulum()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uCT1o9zzF-Nn" + }, + "outputs": [], + "source": [ + "# Create a single-link pendulum.\n", + "pendulum = osim.ModelFactory.createPendulum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following image shows the structure of the pendulum. It consists of a body attached to the ground through a pin joint.\n", + "\n", + "![215195548-9c713166-5cac-4ac4-9a7a-728ce74ff074.svg](data:image/svg+xml;base64,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+ ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bMYcGGuaetwS" + }, + "source": [ + "Before simulating a `Model`, you must initialize the system. Use the `initSystem()` method, which both builds the underlying computational system and constructs a `SimTK::State` containing default values for the `Model`'s coordinates (i.e., joint angles and speeds)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "horV86-lePLd" + }, + "outputs": [], + "source": [ + "# Initialize the system and obtain the initial state.\n", + "state = pendulum.initSystem()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xU1CWpsffGHM" + }, + "source": [ + "The `Manager` class is the primary tool for creating a time-stepping simulation with an OpenSim `Model`. It manages the integration of the `Model`s system and records a trajectory of states. Run the following cell to construct a `Manager` object, simulate the pendulum model, and export a `TimeSeriesTable` containing the states trajectory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QIpWVzXOfGbX" + }, + "outputs": [], + "source": [ + "# Create a new Manager object for the pendulum model.\n", + "manager = osim.Manager(pendulum)\n", + "\n", + "# Initialize the Manager using the state we obtained from Model::initSystem(). This sets\n", + "# the initial conditions for the simulation. The pendulum will start at rest positioned\n", + "# 90 degrees from the vertical, and therefore will swing down under the influence of \n", + "# gravity during the simulation.\n", + "manager.initialize(state)\n", + "\n", + "# Call Manager's \"integrate()\" method to simulate the pendulum for 10 seconds.\n", + "state = manager.integrate(10.0)\n", + "\n", + "# Obtain a table containing the states of the simulation.\n", + "statesTable = manager.getStatesTable()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KFYZ4O9XN4xH" + }, + "source": [ + "Next, we will extract the times and joint angles from the `TimeSeriesTable` returned by the `Manager`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FPACtscIGPaC" + }, + "outputs": [], + "source": [ + "# Extract time values for the x-axis.\n", + "times = statesTable.getIndependentColumn()\n", + "\n", + "# Extract the joint angle of the pendulum over time.\n", + "angles = statesTable.getDependentColumn('/jointset/j0/q0/value')\n", + "\n", + "# Print number of states\n", + "print(f'Number of simulation states in the time interval [0, {state.getTime()}]: {angles.size()}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EeSYY1DtOLhd" + }, + "source": [ + "The data from the simulation can now be plotted using, for example, `matplotlib`. The following cell plots the joint angle of the pendulum over time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NX4GrIpXGP1F" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Plot pendulum's joint angle over time.\n", + "plt.title(\"Pendulum Simulation\")\n", + "plt.plot(times, angles.to_numpy())\n", + "plt.xlabel(\"time (s)\")\n", + "plt.ylabel(\"angle (rad)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J5mhS9zNX9ff" + }, + "source": [ + "## 1.4. Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_M04jPJLuiUz" + }, + "source": [ + "In this tutorial you used [condacolab](https://github.com/conda-incubator/condacolab) to install [Miniconda](https://docs.conda.io/en/latest/miniconda.html) in [Google Colab](https://colab.research.google.com/?hl=en). Then, you installed Conda [OpenSim](https://opensim.stanford.edu/) in the Miniconda environment, and you tested it by creating and simulating a simple pendulum. Finally, you plotted the positions of the pendulum using [matplotlib](https://matplotlib.org/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lLWgd8Noun6Y" + }, + "source": [ + "## 1.5. Useful Links\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m8M3dlT5xQcD" + }, + "source": [ + "> **OpenSim Website:** https://opensim.stanford.edu/\n", + ">\n", + "> **OpenSim Python Scripting:** https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53085346/Scripting+in+Python\n", + ">\n", + "> **OpenSim API Documentation:** https://simtk.org/api_docs/opensim/api_docs/\n", + "> \n", + "> **OpenSim Creator Website:** https://opensimcreator.com/\n", + "> \n", + "> **SimTK Website:** https://simtk.org/projects/opensim\n", + "> \n", + "> **Biomechanics of Movement Course Videos:** https://www.youtube.com/channel/UCDNGy0KKNLQ-ztcL5h2Z6zA" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rFR2Rf9tk_9A" + }, + "source": [ + "## 1.6 Acknowledgments\n", + "\n", + "Thanks to [OpenSimColab](https://simtk.org/projects/opencolab) project [[2]](https://doi.org/10.1080/10255842.2022.2104607) for creating the first OpenSim conda package." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dxMjdukAnH2N" + }, + "source": [ + "## 1.7. References\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WX2A_iaYulV1" + }, + "source": [ + "> [1] Delp, S. L., Anderson, F. C., Arnold, A. S., Loan, P., Habib, A., John, C. T., Guendelman, E., & Thelen, D. G. (2007). **OpenSim: open-source software to create and analyze dynamic simulations of movement.** *IEEE Transactions on Bio-Medical Engineering*, 54(11), 1940–1950. https://doi.org/10.1109/TBME.2007.901024\n", + ">\n", + "> [2] Mokhtarzadeh, H., Jiang, F., Zhao, S., & Malekipour, F. (2022). **OpenColab project: OpenSim in Google colaboratory to explore biomechanics on the web.** *Computer Methods in Biomechanics and Biomedical Engineering*, 1–9. https://doi.org/10.1080/10255842.2022.2104607" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "opensim", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Bindings/Python/tutorials/Tutorial 2 - Creating and Simulating a simple arm Model.ipynb b/Bindings/Python/tutorials/Tutorial 2 - Creating and Simulating a simple arm Model.ipynb new file mode 100644 index 0000000000..90df282792 --- /dev/null +++ b/Bindings/Python/tutorials/Tutorial 2 - Creating and Simulating a simple arm Model.ipynb @@ -0,0 +1,613 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "p60SkX5SZXDS" + }, + "source": [ + "# 2. Creating and Simulating a Simple Arm Model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YLK6vcNo1NHP" + }, + "source": [ + "## 2.1. Objectives" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TKiEOjfi1LW1" + }, + "source": [ + "**Purpose**\n", + "\n", + "The purpose of this tutorial is to introduce you to the creation of musculoskeletal models using [OpenSim](https://opensim.stanford.edu/) [[1]](https://doi.org/10.1109/TBME.2007.901024). In this tutorial you will:\n", + "\n", + "* Create a simple arm model.\n", + "* Use the model to perform a forward simulation.\n", + "* Plot information obtained from the simulation.\n", + "\n", + "**Format**\n", + "\n", + "In this tutorial, you will create an arm model. The model is very simple and consists of two bones (humerus and radius), and a muscle to flex the arm (biceps).\n", + "\n", + "The first step of this tutorial will be to set up the environment (See ***Tutorial 1: Introduction to OpenSim***), and then you will programmatically create the model by defining the necessary bodies, muscles, and joints. Finally, you will perform Forward Kinematics [[2]](https://mitpress.mit.edu/9780262044202/biomechanics-of-movement/) with the model to obtain kinematic data.\n", + "\n", + "**Local Installation**\n", + "\n", + "You may also run this notebook using a locally installed OpenSim `conda` package. Visit [the OpenSim python scripting Confluence page](https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53085346/Scripting+in+Python) for more information on how to install OpenSim in a local Python environment. Then, you may skip to Section 2.3 below." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ADllcxbsjTr8" + }, + "source": [ + "## 2.2. Setup `conda` and OpenSim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mmmTuEI4HQJG" + }, + "source": [ + "First, set up the environment by executing the following cell (See [Tutorial 1: Introduction to OpenSim](https://colab.research.google.com/github/nickbianco/opensim-core/blob/python_examples/Bindings/Python/tutorials/Tutorial%201%20-%20Introduction%20to%20OpenSim.ipynb))." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kdnS0S_xioqg" + }, + "outputs": [], + "source": [ + "!pip install -q condacolab\n", + "import condacolab\n", + "condacolab.install()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vt5loGbdfKb8" + }, + "source": [ + "Don't worry if after executing the previous cell you get an error saying that your session has failed. This happens because `condacolab` needs to restart the session for the changes to take effect. Therefore, you have to execute the previous cell before executing any other cell.\n", + "\n", + "Now, execute the following cell to install the OpenSim `conda` package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6ZnEPfPgjup9" + }, + "outputs": [], + "source": [ + "!conda install opensim-org::opensim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q85J8kOTj69D" + }, + "source": [ + "## 2.3. Create the model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U_hlEyz5HT4E" + }, + "source": [ + "First, import OpenSim and create an empty `Model` named \"simple_arm\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ChnLbJ0CkSH7" + }, + "outputs": [], + "source": [ + "import opensim as osim\n", + "\n", + "# Create empty model and assign to it the name \"simple_arm\".\n", + "arm = osim.Model()\n", + "arm.setName(\"simple_arm\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ENIL146IlcUk" + }, + "source": [ + "Next, create two bodies (the bones) using the `Body` class constructor function ([documentation](https://simtk.org/api_docs/opensim/api_docs/classOpenSim_1_1Body.html#ab1d061783d6e0b093e0794c19326e7c0)). This function takes 4 inputs:\n", + " * **name**: use \"humerus\" and \"radius\" for the names of the two bodies\n", + " * **mass**: set a mass of 1 $kg$ for both bodies\n", + " * **center of mass location**: set the location to be the body's origin (i.e., $(0,0,0)$) in both bodies (`osim.Vec3(0)`)\n", + " * **inertia**: use an inertia matrix with moments of inertia of 0.1, 0.5 and 0.1 $kg \\cdot m^2$, in the x, y and z directions, respectively, and with products of inertia of 0 (`osim.Inertia(0.1, 0.5, 0.1)`)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JP4vs1OtlfOa" + }, + "outputs": [], + "source": [ + "# Define the humerus.\n", + "humerus = osim.Body(\"humerus\",\n", + " 1.0,\n", + " osim.Vec3(0),\n", + " osim.Inertia(0.1, 0.5, 0.1))\n", + "\n", + "# Define the radius.\n", + "radius = osim.Body(\"radius\",\n", + " 1.0,\n", + " osim.Vec3(0),\n", + " osim.Inertia(0.1, 0.5, 0.1))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JZM8_weblhfc" + }, + "source": [ + "The model needs pin joints, and we'll use the `PinJoint` class constructor function (which uses the same input as `Joint`, [documentation](https://simtk.org/api_docs/opensim/api_docs/classOpenSim_1_1Joint.html#a31854c22f626c982ad67fcff12a94961)) to create two pin joints. The first pin joint corresponds with the shoulder, and connects the top of the humerus with the ground (the humerus is the root of the model). The second pin joint corresponds with the elbow and connects the bottom of the humerus with the top of the radius. Assume each body is 1 $m$ long." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QxAX4pUrli0N" + }, + "outputs": [], + "source": [ + "# Shoulder pin joint from the ground to the humerus, so it is fixed and will not fall.\n", + "shoulder = osim.PinJoint(\"shoulder\",\n", + " arm.getGround(), # PhysicalFrame\n", + " osim.Vec3(0),\n", + " osim.Vec3(0),\n", + " humerus, # PhysicalFrame\n", + " osim.Vec3(0, 0.5, 0), # attach the joint 0.5 m up from the humerus origin\n", + " osim.Vec3(0))\n", + "\n", + "# Elbow pin joint from the humerus to the radius.\n", + "elbow = osim.PinJoint(\"elbow\",\n", + " humerus, # PhysicalFrame\n", + " osim.Vec3(0, -0.5, 0), # attach the joint 0.5 m down from the humerus origin\n", + " osim.Vec3(0),\n", + " radius, # PhysicalFrame\n", + " osim.Vec3(0, 0.5, 0), # attach the joint 0.5 m up from the radius origin\n", + " osim.Vec3(0))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MmIGq-lYlk2W" + }, + "source": [ + "In order to flex the elbow, now you will add a muscle representing the biceps to the model. This muscle is an actuator that can be actively controlled to generate force. \n", + "\n", + "The muscle is created as a configurable muscle using the `Millard2012EquilibriumMuscle` class [[3]](https://doi.org/10.1115/1.4023390), and has a maximum isometric force of 100 $N$, optimal fiber length of 0.6 $m$, tendon slack length of 0.55 $m$, and a pennation angle of 0.0 $rad$. Then, two path points are defined with muscle's origin point in the humerus and insertion point in the radius.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BkNnZOJtllMu" + }, + "outputs": [], + "source": [ + "# Create and set the parameters for the biceps muscle.\n", + "biceps = osim.Millard2012EquilibriumMuscle(\"biceps\", # Muscle name\n", + " 100.0, # Max isometric force\n", + " 0.6, # Optimal fiber length\n", + " 0.55, # Tendon slack length\n", + " 0.0) # Pennation angle\n", + "\n", + "# Add path points to the humerus and radius. The allows the muscle to generate\n", + "# forces on these two bodies.\n", + "biceps.addNewPathPoint(\"origin\",\n", + " humerus,\n", + " osim.Vec3(0, 0.3, 0))\n", + "biceps.addNewPathPoint(\"insertion\",\n", + " radius,\n", + " osim.Vec3(0, 0.2, 0))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "w4dbDoA6loHI" + }, + "source": [ + "In order to excite the muscle (activate the actuator), the model needs a controller. In the following cell you will create a `PrescribedController`, add the biceps as an actuator to the controller, and specify the excitation of the muscle. In this example, the excitation is time-varying and defined by a step function ([documentation](https://simtk.org/api_docs/opensim/api_docs/classOpenSim_1_1StepFunction.html#afdce40906fc61bc7dbcfa026d1f0ad8b)).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IKcMjiBIlpY2" + }, + "outputs": [], + "source": [ + "# Create a controller, add the actuator (muscle) to it, and specify the excitation.\n", + "brain = osim.PrescribedController()\n", + "brain.addActuator(biceps)\n", + "brain.prescribeControlForActuator(\"biceps\", osim.StepFunction(0.5, 3.0, 0.3, 1.0))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9YB5snO5lruj" + }, + "source": [ + "Now that every component of the model has been defined, let's add all of the elements to the arm model. Remember that the elements that you have defined are the bodies (bones) of the model, the joints of the model, an actuator (muscle) that generates force in the model, and a controller (brain) that specifies how the actuator is activated throughout the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "glSBsxAaltLg" + }, + "outputs": [], + "source": [ + "# Add all of the elements defined in previous cells to the model.\n", + "arm.addBody(humerus)\n", + "arm.addBody(radius)\n", + "arm.addJoint(shoulder)\n", + "arm.addJoint(elbow)\n", + "arm.addForce(biceps)\n", + "arm.addController(brain)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wGKY1uv2l3vt" + }, + "source": [ + "After building the model, you have to initialize the system in order to access and change the state of the model. \n", + "\n", + "We lock the shoulder joint so that it will not change its angle during the simulation. We set the elbow angle to an initial value of 90º, or `0.5 * π` $rad$. Finally, we update the state of the muscle, so that the force in the muscle and in the tendon are in equilibrium before we start the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "npMwjT5ol3_V" + }, + "outputs": [], + "source": [ + "# Initialize model.\n", + "state = arm.initSystem()\n", + "\n", + "# Fix the shoulder at its default angle and begin with the elbow flexed 90º degrees.\n", + "shoulder.getCoordinate().setLocked(state, True)\n", + "elbow.getCoordinate().setValue(state, 0.5 * osim.SimTK_PI)\n", + "arm.equilibrateMuscles(state)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MqQqxE20l-Yd" + }, + "source": [ + "Now, you can simulate the model. First, let's create a `Manager` and set the starting time to the state. The `Manager` is an object that manages the execution of the simulation. Using the manager, you can then perform a \"forward simulation\" [[2]](https://mitpress.mit.edu/9780262044202/biomechanics-of-movement/), where the manager integrates the equations of motion for the specified model forward in time, given the current state and a final time. This will return a new state with the results of the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vBpKDPgsl_Zh" + }, + "outputs": [], + "source": [ + "# Initialize the system and set initial time.\n", + "state.setTime(0)\n", + "\n", + "# Get and initialize manager with the state.\n", + "manager = osim.Manager(arm)\n", + "manager.initialize(state)\n", + "\n", + "# Integrate the model until the ending time.\n", + "state = manager.integrate(10.0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XEumXmZyWQ2b" + }, + "source": [ + "After the simulation is finished, you can extract the table of states. Let's also print the column labels, so we have the paths to the states of the values that we are interested in plotting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "mbLScbezWa2W" + }, + "outputs": [], + "source": [ + "# Extract table of states.\n", + "statesTable = manager.getStatesTable()\n", + "\n", + "# Print labels of columns in the table.\n", + "print(statesTable.getColumnLabels())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KqD7waFqRo_W" + }, + "source": [ + "We want to plot the shoulder and elbow positions and speed, as well as the biceps activation and fiber length over time. Let's extract the desired data from the table of states." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HNWVlK-VRp2C" + }, + "outputs": [], + "source": [ + "# Extract time values for the x-axis.\n", + "times = statesTable.getIndependentColumn()\n", + "\n", + "# Extract shoulder data.\n", + "shoulder_pos = statesTable.getDependentColumn('/jointset/shoulder/shoulder_coord_0/value')\n", + "shoulder_speed = statesTable.getDependentColumn('/jointset/shoulder/shoulder_coord_0/speed')\n", + "\n", + "# Extract elbow data.\n", + "elbow_pos = statesTable.getDependentColumn('/jointset/elbow/elbow_coord_0/value')\n", + "elbow_speed = statesTable.getDependentColumn('/jointset/elbow/elbow_coord_0/speed')\n", + "\n", + "# Extract biceps data.\n", + "biceps_activation = statesTable.getDependentColumn('/forceset/biceps/activation')\n", + "biceps_fiber_length = statesTable.getDependentColumn('/forceset/biceps/fiber_length')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PAJ0FRKHardS" + }, + "source": [ + "## 2.4. Plotting data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D-iFifvcVR5U" + }, + "source": [ + "In this section, you will plot the results of the simulation using a well known python library: [Matplotlib](https://matplotlib.org/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kX6MdpfEa3aS" + }, + "source": [ + "### 2.4.1. Matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-Vu58PadWMQh" + }, + "source": [ + "The following cell plots the desired columns (`shoulder_pos`, `shoulder_speed`, `elbow_pos`, `elbow_speed`, `biceps_act` and `biceps_fib_len`) using `matplotlib`. Each column is plotted in a different subplot.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HuquEKo-a4_E" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(2, 3, figsize=(14, 8))\n", + "fig.suptitle('Arm model data representation')\n", + "\n", + "# Plot shoulder position and speed in column 1.\n", + "axs[0, 0].plot(times, shoulder_pos.to_numpy())\n", + "axs[0, 0].set_title('Shoulder pos.')\n", + "axs[0, 0].set_xlabel('Time')\n", + "axs[0, 0].set_ylabel('Position')\n", + "axs[0, 0].grid()\n", + "axs[1, 0].plot(times, shoulder_speed.to_numpy())\n", + "axs[1, 0].set_title('Shoulder speed')\n", + "axs[1, 0].set_xlabel('Time')\n", + "axs[1, 0].set_ylabel('Speed')\n", + "axs[1, 0].grid()\n", + "\n", + "# Plot elbow position and speed in column 2.\n", + "axs[0, 1].plot(times, elbow_pos.to_numpy())\n", + "axs[0, 1].set_title('Elbow pos.')\n", + "axs[0, 1].set_xlabel('Time')\n", + "axs[0, 1].set_ylabel('Position')\n", + "axs[0, 1].grid()\n", + "axs[1, 1].plot(times, elbow_speed.to_numpy())\n", + "axs[1, 1].set_title('Elbow speed')\n", + "axs[1, 1].set_xlabel('Time')\n", + "axs[1, 1].set_ylabel('Speed')\n", + "axs[1, 1].grid()\n", + "\n", + "# Plot biceps activation and length in column 3.\n", + "axs[0, 2].plot(times, biceps_activation.to_numpy())\n", + "axs[0, 2].set_title('Biceps act.')\n", + "axs[0, 2].set_xlabel('Time')\n", + "axs[0, 2].set_ylabel('Activation')\n", + "axs[0, 2].grid()\n", + "axs[1, 2].plot(times, biceps_fiber_length.to_numpy())\n", + "axs[1, 2].set_title('Biceps fiber length')\n", + "axs[1, 2].set_xlabel('Time')\n", + "axs[1, 2].set_ylabel('Fiber length')\n", + "axs[1, 2].grid()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i7rhPT2id6gg" + }, + "source": [ + "## 2.5. Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1XK9UgItWQMF" + }, + "source": [ + "In this tutorial you have created a simple arm model defined by two bones (bodies), two joints, and a muscle (actuator). Then, you have simulated the model and plotted the calculated values.\n", + "\n", + "To create more complex models graphically and visualize them, check out [OpenSim Creator](https://opensimcreator.com/). OpenSim Creator is freely available, open-source software that enables users to visually build, edit, and simulate OpenSim models." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lLWgd8Noun6Y" + }, + "source": [ + "## 2.6. Useful Links\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m8M3dlT5xQcD" + }, + "source": [ + "> **OpenSim Website:** https://opensim.stanford.edu/\n", + ">\n", + "> **OpenSim Python Scripting:** https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53085346/Scripting+in+Python\n", + ">\n", + "> **OpenSim API Documentation:** https://simtk.org/api_docs/opensim/api_docs/\n", + "> \n", + "> **OpenSim Creator Website:** https://opensimcreator.com/\n", + "> \n", + "> **SimTK Website:** https://simtk.org/projects/opensim\n", + "> \n", + "> **Biomechanics of Movement Course Videos:** https://www.youtube.com/channel/UCDNGy0KKNLQ-ztcL5h2Z6zA" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rFR2Rf9tk_9A" + }, + "source": [ + "## 2.7 Acknowledgments\n", + "\n", + "Thanks to [OpenSimColab](https://simtk.org/projects/opencolab) project [[4]](https://doi.org/10.1080/10255842.2022.2104607) for creating the first OpenSim Conda package." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dxMjdukAnH2N" + }, + "source": [ + "## 2.8. References\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WX2A_iaYulV1" + }, + "source": [ + "> [1] Delp, S. L., Anderson, F. C., Arnold, A. S., Loan, P., Habib, A., John, C. T., Guendelman, E., & Thelen, D. G. (2007). **OpenSim: open-source software to create and analyze dynamic simulations of movement.** *IEEE Transactions on Bio-Medical Engineering*, 54(11), 1940–1950. https://doi.org/10.1109/TBME.2007.901024\n", + ">\n", + "> [2] Uchida, T. K., Delp, S. L., & Delp, D. (2021). **Biomechanics of movement: The science of sports, robotics, and rehabilitation.** *MIT Press*. https://mitpress.mit.edu/9780262044202/biomechanics-of-movement/\n", + ">\n", + "> [3] Millard, M., Uchida, T., Seth, A., & Delp, S. L. (2013). **Flexing computational muscle: Modeling and simulation of musculotendon dynamics.** *Journal of Biomechanical Engineering*, 135(2). https://doi.org/10.1115/1.4023390\n", + ">\n", + "> [4] Mokhtarzadeh, H., Jiang, F., Zhao, S., & Malekipour, F. (2022). **OpenColab project: OpenSim in Google colaboratory to explore biomechanics on the web.** *Computer Methods in Biomechanics and Biomedical Engineering*, 1–9. https://doi.org/10.1080/10255842.2022.2104607" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "opensim", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Bindings/Python/tutorials/Tutorial 3 - Loading and Modifying OpenSim Models.ipynb b/Bindings/Python/tutorials/Tutorial 3 - Loading and Modifying OpenSim Models.ipynb new file mode 100644 index 0000000000..fc08cbae00 --- /dev/null +++ b/Bindings/Python/tutorials/Tutorial 3 - Loading and Modifying OpenSim Models.ipynb @@ -0,0 +1,484 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1dzvRcZuYhsP" + }, + "source": [ + "# 3. Loading and Modifying OpenSim Models" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YLK6vcNo1NHP" + }, + "source": [ + "## 3.1. Objectives" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TKiEOjfi1LW1" + }, + "source": [ + "**Purpose**\n", + "\n", + "In this tutorial, you will learn to load and modify `Model`s in [OpenSim](https://opensim.stanford.edu/) [[1]](https://doi.org/10.1109/TBME.2007.901024), as well as to access and modify the data in those models. This tutorial will lead you through the following:\n", + "\n", + "* Loading an existing `Model`.\n", + "* Accessing and modifying `Model` property.\n", + "* Saving a `Model`.\n", + "\n", + "**Format**\n", + "\n", + "You will first download a model and load it into OpenSim. Then, you will access and print information from the model. Finally, you will modify the model and save and download it to your computer. \n", + "\n", + "**Resources**\n", + "\n", + "All of the files necessary to complete this tutorial are available in the following Google Drive folder: https://drive.google.com/drive/folders/18wYREuiX0jWYEmZhfgL6OCMJMJDv86lJ?usp=sharing\n", + "\n", + "While you complete the tutorial, you will find cells that automatically download the required files.\n", + "\n", + "To obtain a link to one of the files in Google Drive, right-click over it and click on **Get Link**.\n", + "\n", + "**Local Installation**\n", + "\n", + "You may also run this notebook using a locally installed OpenSim `conda` package. Visit [the OpenSim python scripting Confluence page](https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53085346/Scripting+in+Python) for more information on how to install OpenSim in a local Python environment. You will also need to download the resources from Google Drive to the same directory as this notebook. Then, you may skip to Section 3.3 below." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ADllcxbsjTr8" + }, + "source": [ + "## 3.2. Setup `conda` and OpenSim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mmmTuEI4HQJG" + }, + "source": [ + "First, set up the environment by executing the following cell (see [Tutorial 1: Introduction to OpenSim](https://colab.research.google.com/github/nickbianco/opensim-core/blob/python_examples/Bindings/Python/tutorials/Tutorial%201%20-%20Introduction%20to%20OpenSim.ipynb) for more details)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uYZPRpAQxXmp" + }, + "outputs": [], + "source": [ + "!pip install -q condacolab\n", + "import condacolab\n", + "condacolab.install()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vt5loGbdfKb8" + }, + "source": [ + "Don't worry if after executing the previous cell you get an error saying that your session has failed. This happens because `condacolab` needs to restart the session for the changes to take effect. Therefore, you have to execute the previous cell before executing any other cell.\n", + "\n", + "Now, execute the following cell to install the OpenSim `conda` package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6l59_J_U8ubr" + }, + "outputs": [], + "source": [ + "!conda install opensim-org::opensim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4gtTNdaO8wOI" + }, + "source": [ + "## 3.3. Load an OpenSim `Model`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qQhpl9pUcgPf" + }, + "source": [ + "In this section, you will download an OpenSim `Model` representing a double pendulum, load it into memory using the OpenSim API, and explore some of its properties." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TIQp3TtGYMyQ" + }, + "source": [ + "First, let's download the model file (`double_pendulum.osim`). The file is stored in a Google Drive folder (See **Resources** in the Objectives section)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LjMgzeHLYa2W" + }, + "outputs": [], + "source": [ + "!gdown \"1lxuQ7PI9aiv9Mr4G1xj31PuBn3i6HjJI&confirm=t\" # double_pendulum.osim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_F6Hrnjba7vL" + }, + "source": [ + "Now, let's import OpenSim, load the model, and print some metadata." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fmGIZ0Hl8z_G" + }, + "outputs": [], + "source": [ + "import opensim as osim\n", + "\n", + "# Load the model.\n", + "double_pendulum = osim.Model('double_pendulum.osim')\n", + "\n", + "# Print metadata.\n", + "print(\"Name of the model:\", double_pendulum.getName())\n", + "print(\"Author:\", double_pendulum.get_credits())\n", + "print(\"Publications:\", double_pendulum.get_publications())\n", + "print(\"Length Unit:\", double_pendulum.get_length_units())\n", + "print(\"Force Unit:\", double_pendulum.get_force_units())\n", + "print(\"Gravity:\", double_pendulum.get_gravity())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TQG_Qc9_NDsd" + }, + "source": [ + "Let's explore some of the model's body properties. Since it is a double pendulum, we expect the model to have two bodies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tSs587JhVu5j" + }, + "outputs": [], + "source": [ + "# Print the number of bodies.\n", + "print(\"Num Bodies:\", double_pendulum.getNumBodies())\n", + "print()\n", + "\n", + "# For each body, print some information, such as its name and mass.\n", + "for body in double_pendulum.getBodySet():\n", + " print(\"Body Name:\", body.getName())\n", + " print(\"Body Absolute Path:\", body.getAbsolutePathString())\n", + " print(\"Body Mass:\", body.getMass())\n", + " print(\"Body Mass Center:\", body.getMassCenter())\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v_xgSNv9Yz2m" + }, + "source": [ + "Let's explore some of the model's joint properties. Again, we expect to have two joints. The first joint connects the ground to `rod1`, so the model does not fall when simulated. The second joint connects `rod1` to `rod2`, the two bodies of the double pendulum." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2laouerdY59s" + }, + "outputs": [], + "source": [ + "# Print the number of joints.\n", + "print(\"Num Joints:\", double_pendulum.getNumJoints())\n", + "print()\n", + "\n", + "# For each joint, print some information, such as its name or components that\n", + "# this joint owns.\n", + "for joint in double_pendulum.getJointSet():\n", + " print(\"Joint Name:\", joint.getName())\n", + " print(\"Joint Absolute Path:\", joint.getAbsolutePathString())\n", + " print(\"Components:\")\n", + " for component in joint.getComponentsList():\n", + " print(\" Component Name:\", component.getName())\n", + " print(\" Component Absolute Path:\", component.getAbsolutePathString())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hW93EJzR9dFx" + }, + "source": [ + "Finally, let's explore the coordinates of the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dj2PFUrD9gbF" + }, + "outputs": [], + "source": [ + "# Print the number of coordinates.\n", + "print(\"Num Coordinates:\", double_pendulum.getNumCoordinates())\n", + "print()\n", + "\n", + "# For each coordinate, print some information, such as its name or motion type.\n", + "for coordinate in double_pendulum.getCoordinateSet():\n", + " print(\" Coordinate Name:\", coordinate.getName())\n", + " print(\" Coordinate Absolute Path:\", coordinate.getAbsolutePathString())\n", + " \n", + " # Motion type is represented using an enum (0:Undefined, 1:Rotational, \n", + " # 2:Translational, 3:Coupled).\n", + " motion_type = coordinate.getMotionType()\n", + " motion_type_string = \"\"\n", + " if motion_type == 0:\n", + " motion_type_string = \"Undefined\"\n", + " elif motion_type == 1:\n", + " motion_type_string = \"Rotational\"\n", + " elif motion_type == 2:\n", + " motion_type_string = \"Translational\"\n", + " elif motion_type == 3:\n", + " motion_type_string = \"Coupled\"\n", + " print(\" Coordinate Motion Type:\", motion_type_string)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v_I5gYAdazmy" + }, + "source": [ + "## 3.4. Modify the `Model`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "p_zHk2nBctVE" + }, + "source": [ + "Let's modify some of the model's properties. For example, let's set the model's gravity to be gravity on the planet Mars (3.72 $m/s^2$), and double the mass of the bodies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Q72WxmqOa64r" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# Set gravity to that of planet Mars (3.72 m/s^2).\n", + "double_pendulum.set_gravity(osim.Vec3(0, -3.72, 0))\n", + "print(\"New Gravity:\", double_pendulum.get_gravity())\n", + "print()\n", + "\n", + "# For each body, print its name, set the mass as 2 kg, and print the updated mass.\n", + "for body in double_pendulum.getBodyList():\n", + " print(\"Body Name:\", body.getName())\n", + " body.setMass(2.0)\n", + " print(\"Body New Mass:\", body.getMass())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o5CJiJePnF-4" + }, + "source": [ + "Now, let's save the model into an `osim` file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "gL0mAwSnbBNi" + }, + "outputs": [], + "source": [ + "double_pendulum.printToXML('heavy-double-pendulum-mars.osim');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vpAWFZGHnXzH" + }, + "source": [ + "The file can be downloaded in the Files pane onthe left by `right-clicking` on it and clicking `download` (if the file is not in the tree at the left, you may need to `right-click` and `refresh`).\n", + "\n", + "Alternatively, you can download the file programmatically as shown in the following cell." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YoBp15HOnrGV" + }, + "outputs": [], + "source": [ + "from google.colab import files\n", + "\n", + "files.download('heavy-double-pendulum-mars.osim') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If working locally, the file will be saved to the current working directory." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i7rhPT2id6gg" + }, + "source": [ + "## 3.5. Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1XK9UgItWQMF" + }, + "source": [ + "In this tutorial, you downloaded a double pendulum model and loaded it into OpenSim. Then, you explored and modified some of its properties and components. Finally, you saved the updated model into an `.osim` file." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lLWgd8Noun6Y" + }, + "source": [ + "## 3.6. Useful Links\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m8M3dlT5xQcD" + }, + "source": [ + "> **OpenSim Website:** https://opensim.stanford.edu/\n", + ">\n", + "> **OpenSim Python Scripting:** https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53085346/Scripting+in+Python\n", + ">\n", + "> **OpenSim API Documentation:** https://simtk.org/api_docs/opensim/api_docs/\n", + "> \n", + "> **OpenSim Creator Website:** https://opensimcreator.com/\n", + "> \n", + "> **SimTK Website:** https://simtk.org/projects/opensim\n", + "> \n", + "> **Biomechanics of Movement Course Videos:** https://www.youtube.com/channel/UCDNGy0KKNLQ-ztcL5h2Z6zA" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rFR2Rf9tk_9A" + }, + "source": [ + "## 3.7 Acknowledgments\n", + "\n", + "Thanks to [OpenSimColab](https://simtk.org/projects/opencolab) project [[2]](https://doi.org/10.1080/10255842.2022.2104607) for creating the first OpenSim Conda package." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dxMjdukAnH2N" + }, + "source": [ + "## 3.8. References\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WX2A_iaYulV1" + }, + "source": [ + "> [1]. Delp, S. L., Anderson, F. C., Arnold, A. S., Loan, P., Habib, A., John, C. T., Guendelman, E., & Thelen, D. G. (2007). **OpenSim: open-source software to create and analyze dynamic simulations of movement.** *IEEE Transactions on Bio-Medical Engineering*, 54(11), 1940–1950. https://doi.org/10.1109/TBME.2007.901024\n", + ">\n", + "> [2] Mokhtarzadeh, H., Jiang, F., Zhao, S., & Malekipour, F. (2022). **OpenColab project: OpenSim in Google colaboratory to explore biomechanics on the web.** *Computer Methods in Biomechanics and Biomedical Engineering*, 1–9. https://doi.org/10.1080/10255842.2022.2104607" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "opensim", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Bindings/Python/tutorials/Tutorial 4 - Musculoskeletal models, Motion Files and MuscleAnalysis.ipynb b/Bindings/Python/tutorials/Tutorial 4 - Musculoskeletal models, Motion Files and MuscleAnalysis.ipynb new file mode 100644 index 0000000000..5f0258a167 --- /dev/null +++ b/Bindings/Python/tutorials/Tutorial 4 - Musculoskeletal models, Motion Files and MuscleAnalysis.ipynb @@ -0,0 +1,995 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vOsTxESLc7wl" + }, + "source": [ + "# 4. Musculoskeletal Models, Motion Files, and `MuscleAnalysis`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SwX6QwGU0Jwj" + }, + "source": [ + "## 4.1. Objectives" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4p_3qlB60Np3" + }, + "source": [ + "**Introduction**\n", + "\n", + "Musculoskeletal models enable us to study neuromuscular coordination, analyze athletic performance, and estimate musculoskeletal loads [[1]](https://mitpress.mit.edu/9780262044202/biomechanics-of-movement/). In [OpenSim](https://opensim.stanford.edu/) [[2]](https://doi.org/10.1109/TBME.2007.901024), a musculoskeletal model consists of rigid body segments connected by joints. Muscles span these joints and generate forces and motion. Once a musculoskeletal model is created, OpenSim enables users to create custom studies, including investigating the effects of musculoskeletal geometry, joint kinematics, and muscle-tendon properties on the forces and joint moments that the muscles can produce. With OpenSim, our goal is to provide a framework that allows the biomechanics community to create, share, and extend a library of models and dynamic simulation tools that can be used to study and quantify human and animal movement. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4SuUmksVUl5b" + }, + "source": [ + "**Purpose**\n", + "\n", + "The purpose of this tutorial is to introduce users to the OpenSim `conda` package for musculoskeletal modeling by investigating how muscle-tendon lengths and moment arms depend on limb configuration. In this tutorial, you will:\n", + "\n", + "* Become familiar with OpenSim's API.\n", + "* Explore a lower-extremity musculoskeletal model.\n", + "* Work with motion files.\n", + "* Use the `MuscleAnalysis` tool.\n", + "* Use OpenSim to analyze an important clinical problem." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-Ko_AC3hUiqk" + }, + "source": [ + "**Format**\n", + "\n", + "Each section of the tutorial guides you through certain tools within OpenSim and asks you to answer a few questions. The questions can be answered based on information from OpenSim and basic knowledge of the human musculoskeletal system. As you complete each section of the tutorial, feel free to explore OpenSim and the lower extremity model further on your own." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D3abtOlWUewq" + }, + "source": [ + "\n", + "**Resources**\n", + "\n", + "All of the files necessary to complete this tutorial are available in the following Google Drive folder: https://drive.google.com/drive/folders/1oaSigQup8DNEpXX5_Z6Edk7PMKBXvFOq?usp=sharing\n", + "\n", + "While you complete the tutorial, you will find cells that automatically download the required files.\n", + "\n", + "To obtain a link to one of the files in Google Drive, right-click over it and click on **Get Link**.\n", + "\n", + "**Local Installation**\n", + "\n", + "You may also run this notebook using a locally installed OpenSim `conda` package. Visit [the OpenSim python scripting Confluence page](https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53085346/Scripting+in+Python) for more information on how to install OpenSim in a local Python environment. You will also need to download the resources from Google Drive to the same directory as this notebook. Then, you may skip to Section 4.3 below." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ADllcxbsjTr8" + }, + "source": [ + "## 4.2. Set up `conda` and OpenSim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mmmTuEI4HQJG" + }, + "source": [ + "First, set up the environment by executing the following cell (see [Tutorial 1: Introduction to OpenSim](https://colab.research.google.com/github/nickbianco/opensim-core/blob/python_examples/Bindings/Python/tutorials/Tutorial%201%20-%20Introduction%20to%20OpenSim.ipynb) for more information)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0w9LYQXx5TO4" + }, + "outputs": [], + "source": [ + "!pip install -q condacolab\n", + "import condacolab\n", + "condacolab.install()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vt5loGbdfKb8" + }, + "source": [ + "Don't worry if after executing the previous cell you get an error saying that your session has failed. This happens because `condacolab` needs to restart the session for the changes to take effect. Therefore, you have to execute the previous cell before executing any other cell.\n", + "\n", + "Now, execute the following cell to install the OpenSim `conda` package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vWf9ObZJ5TO5" + }, + "outputs": [], + "source": [ + "!conda install opensim-org::opensim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4gtTNdaO8wOI" + }, + "source": [ + "## 4.3. Downloading and exploring a lower-extremity model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SJ2b6Dx10dD5" + }, + "source": [ + "In this section, you will download a model of the lower extremity [[3]](https://doi.org/10.1109/10.102791), and then load it into OpenSim. The model represents an adult subject with an approximate height of 1.8 m and an approximate mass of 75 kg. The model consists of 13 rigid body segments and includes the lines of action of 92 muscles (43 per leg and 6 at the torso)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TIQp3TtGYMyQ" + }, + "source": [ + "First, let's download the model file (`gait2392.osim`). The file is stored in a Google Drive folder (See **Resources** in the Objectives section)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LjMgzeHLYa2W" + }, + "outputs": [], + "source": [ + "!gdown \"1I7iCmCgrOj8mq8TV1n-r6JA1pFK6Q_z-&confirm=t\" # gait2392.osim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_F6Hrnjba7vL" + }, + "source": [ + "Now, let's import OpenSim, load the model, and print some metadata." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fmGIZ0Hl8z_G" + }, + "outputs": [], + "source": [ + "import opensim as osim\n", + "\n", + "# Load the model.\n", + "gait2392 = osim.Model('gait2392.osim')\n", + "\n", + "# Print metadata.\n", + "print(\"Name of the model:\", gait2392.getName())\n", + "print(\"Author:\", gait2392.get_credits())\n", + "print(\"Publications:\", gait2392.get_publications())\n", + "print(\"Length Unit:\", gait2392.get_length_units())\n", + "print(\"Force Unit:\", gait2392.get_force_units())\n", + "print(\"Gravity:\", gait2392.get_gravity())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hW93EJzR9dFx" + }, + "source": [ + "Let's explore the coordinates of the model. We know the model has 23 coordinates from the name of the model file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dj2PFUrD9gbF" + }, + "outputs": [], + "source": [ + "# Print the number of coordinates.\n", + "print(\"Num Coordinates:\", gait2392.getNumCoordinates())\n", + "print()\n", + "\n", + "# For each coordinate, print some information, such as its name or motion type.\n", + "for coordinate in gait2392.getCoordinateSet():\n", + " print(\" Coordinate Name:\", coordinate.getName())\n", + " print(\" Coordinate Absolute Path:\", coordinate.getAbsolutePathString())\n", + " \n", + " # Motion type is an enumerate (0:Undefined, 1:Rotational, 2:Translational, 3:Coupled).\n", + " motion_type = coordinate.getMotionType()\n", + " motion_type_string = \"\"\n", + " if motion_type == 0:\n", + " motion_type_string = \"Undefined\"\n", + " elif motion_type == 1:\n", + " motion_type_string = \"Rotational\"\n", + " elif motion_type == 2:\n", + " motion_type_string = \"Translational\"\n", + " elif motion_type == 3:\n", + " motion_type_string = \"Coupled\"\n", + " print(\" Coordinate Motion Type:\", motion_type_string)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v_xgSNv9Yz2m" + }, + "source": [ + "Let's explore some of the model's joint properties." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2laouerdY59s" + }, + "outputs": [], + "source": [ + "# Print number of joints.\n", + "print(\"Num Joints:\", gait2392.getNumJoints())\n", + "print()\n", + "\n", + "# For each joint, print some information, such as its name or components.\n", + "for joint in gait2392.getJointSet():\n", + " print(\"Joint Name:\", joint.getName())\n", + " print(\"Joint Absolute Path:\", joint.getAbsolutePathString())\n", + " print(\"Components:\")\n", + " for component in joint.getComponentsList():\n", + " print(\" Component Name:\", component.getName())\n", + " print(\" Component Absolute Path:\", component.getAbsolutePathString())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "30rBKV7S3hzt" + }, + "source": [ + "And finally, let's explore the muscles in the model. We know the model has 92 muscles from the name of the model file.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Etbh4aon3hzt" + }, + "outputs": [], + "source": [ + "# Print the number of muscles.\n", + "print(\"Num Muscles:\", gait2392.getMuscles().getSize())\n", + "print()\n", + "\n", + "for muscle in gait2392.getMuscles():\n", + " print(\"Muscle Name:\", muscle.getName())\n", + " print(\"Muscle Absolute Path:\", muscle.getAbsolutePathString())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D7_wfvV55skF" + }, + "source": [ + "## 4.4. Working with motion files" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FqIgcdg43Qe7" + }, + "source": [ + "In this section, you will load information from a motion file. A motion file (`.mot`) is a tab separated values (tsv) file containing a header with information about the file (e.g., if the values are in degrees, number of columns, etc.), and a set of columns containing how different values in the model evolve over time (e.g., pelvis rotation, pelvis tilt, hip flexion).\n", + "\n", + "Since the motion file already contains the values resulting from a simulation, you will not need to simulate the model." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "55eetMYEn8iK" + }, + "source": [ + "First, let's download the motion file (`normal_gait.mot`). The file is stored in a Google Drive folder (See **Resources** in the Objectives section)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "M9srcF3YadZZ" + }, + "outputs": [], + "source": [ + "!gdown \"1bZq91PPNOHLYz6wIf8l0Zz4igAp54mtu&confirm=t\" # normal_gait.mot" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kFQ-3EmOn-zc" + }, + "source": [ + "Load the file into a `TimeSeriesTable`. Print the column labels to check the file has been processed properly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "CWy88RN8hIMg" + }, + "outputs": [], + "source": [ + "# Use the TableProcessor to read the motion file.\n", + "tableTime = osim.TimeSeriesTable('normal_gait.mot')\n", + "\n", + "# Print the column labels.\n", + "print(tableTime.getColumnLabels())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1OrWnq7ZoEyV" + }, + "source": [ + "In this example, we will plot the knee angle and hip adduction between both legs. Run the following cell to extract this information from the motion file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Cl6bsTwxoHfj" + }, + "outputs": [], + "source": [ + "# Get columns we want to analyze, and the time column (independent column).\n", + "y_knee_angle_r = tableTime.getDependentColumn('knee_angle_r')\n", + "y_knee_angle_l = tableTime.getDependentColumn('knee_angle_l')\n", + "y_hip_adduction_r = tableTime.getDependentColumn('hip_adduction_r')\n", + "y_hip_adduction_l = tableTime.getDependentColumn('hip_adduction_l')\n", + "x_time = tableTime.getIndependentColumn()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "92S8pO4mdUc0" + }, + "source": [ + "We can use Matplotlib to plot the information." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XJt3WB8grQW6" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(2, 1, figsize=(10, 10))\n", + "fig.suptitle('Normal Gait')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs[0].plot(x_time, y_knee_angle_r.to_numpy(), label='knee_angle_r')\n", + "axs[0].plot(x_time, y_knee_angle_l.to_numpy(), label='knee_angle_l')\n", + "axs[0].set_title('Knee angle')\n", + "axs[0].set_xlabel('Time')\n", + "axs[0].set_ylabel('Knee angle')\n", + "axs[0].grid()\n", + "axs[0].legend()\n", + "\n", + "# Plot the hip adductions on the second subplot.\n", + "axs[1].plot(x_time, y_hip_adduction_r.to_numpy(), label='hip_adduction_r')\n", + "axs[1].plot(x_time, y_hip_adduction_l.to_numpy(), label='hip_adduction_l')\n", + "axs[1].set_title('Hip adduction')\n", + "axs[1].set_xlabel('Time')\n", + "axs[1].set_ylabel('Hip Adduction')\n", + "axs[1].grid()\n", + "axs[1].legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iwjKjz4trPYr" + }, + "source": [ + "## 4.5. Using `MuscleAnalysis` to assess hamstring length during crouch gait" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Na1olOGW460V" + }, + "source": [ + "In this final section of the tutorial, you will use OpenSim to investigate a possible cause of crouch gait, one of the most common walking abnormalities among individuals with cerebral palsy. It is characterized by excessive flexion of the knee during stance phase, which is often accompanied by exaggerated flexion and internal rotation of the hip. One hypothesized cause of crouch gait is short hamstrings, and orthopedic surgeons will sometimes lengthen the hamstrings of such patients in an attempt to improve their posture and gait. However, other causes of excessive knee flexion are possible (e.g., weak ankle plantar flexors), and lengthening the hamstrings can compromise these muscles' force-generation capabilities [[4]](https://doi.org/10.1016/j.gaitpost.2005.03.003). How can a surgeon determine whether a hamstring lengthening procedure is warranted? \n", + "\n", + "One possible way to judge whether a patient's hamstrings are shorter than \"normal\" is to develop a musculoskeletal model and compare the length of the hamstrings during the patient's crouch gait cycle to the length of the hamstrings during a normal gait cycle. Suppose that an orthopaedic surgeon has brought you some kinematic data for a patient who walks with a crouch gait. The surgeon is contemplating whether to operate and wants your opinion. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "l393sw739Guj" + }, + "source": [ + "### 4.5.1. Range of Motion" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q4v56Rx-ra29" + }, + "source": [ + "First, let's download the crouch gait motion file (`crouch_gait.mot`).The file is stored in a Google Drive folder (See **Resources** in the Objectives section). We already downloaded the normal gait motion file in the previous section." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "S7rYuVrvbtNQ" + }, + "outputs": [], + "source": [ + "!gdown \"11XGpKPxX4Y6SO4xjPYUQz9gZdjvKyuLH&confirm=t\" # crouch_gait.mot" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1w4Y6NwRra2_" + }, + "source": [ + "Load the motion files into `TimeSeriesTable`s. Print the column labels to check the files have been processed properly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GeZ65ePyra3A" + }, + "outputs": [], + "source": [ + "# Load the normal gait motion file into a TimeSeriesTable.\n", + "table_normal_gait = osim.TimeSeriesTable('normal_gait.mot')\n", + "# Print labels for each column.\n", + "print(table_normal_gait.getColumnLabels())\n", + "\n", + "# Load the crouch gait motion file into a TimeSeriesTable.\n", + "table_crouch_gait = osim.TimeSeriesTable('crouch_gait.mot')\n", + "# Print labels for each column.\n", + "print(table_crouch_gait.getColumnLabels())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lzugVUla-leG" + }, + "source": [ + "Let's obtain the information we want to plot. This time, we obtain two time columns (independent columns), one per each table. Since both have a range from 0 to 1, we can plot them and compare them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "38URdfs9-n-j" + }, + "outputs": [], + "source": [ + "# Get columns we want to represent.\n", + "normal_gait_knee_angle_r = table_normal_gait.getDependentColumn('knee_angle_r')\n", + "crouch_gait_knee_angle_r = table_crouch_gait.getDependentColumn('knee_angle_r')\n", + "\n", + "# Get independent columns of each table (time).\n", + "normal_gait_time = table_normal_gait.getIndependentColumn()\n", + "crouch_gait_time = table_crouch_gait.getIndependentColumn()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8sT3M3Ew-dwa" + }, + "source": [ + "Now let's plot both data series to quantitatively compare." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oaDrdUA5uCC6" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Normal Gait vs. Crouch Gait')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(normal_gait_time, normal_gait_knee_angle_r.to_numpy(), label='normal_gait_knee_angle_r')\n", + "axs.plot(crouch_gait_time, crouch_gait_knee_angle_r.to_numpy(), label='crouch_gait_knee_angle_l')\n", + "axs.set_title('Knee angle')\n", + "axs.set_xlabel('Time')\n", + "axs.set_ylabel('Knee angle')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HU5PSGHT5W1P" + }, + "source": [ + "**Questions: Range of Motion**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CrxnVBkr5lSk" + }, + "source": [ + "\n", + "**1.** Label the times at which heel strike and toe-off occur, and the stance and swing intervals. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oCaTndxk5nQv" + }, + "source": [ + "\n", + "**2.** What is the range of motion for knee flexion during the stance phase for normal gait?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WHJUGwtL5rKR" + }, + "source": [ + "**3.** How does the knee flexion range of motion for crouch gait compare to that of normal gait?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ovoeaVcu95-J" + }, + "source": [ + "### 4.5.2. Hamstrings Length" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VfBhbJtr9E1G" + }, + "source": [ + "An orthopedic surgeon believes that a patient may benefit from a hamstring lengthening surgery. You are tasked to compare the hamstrings (semitendinosus) length over a patient's crouch gait cycle to the hamstrings length for a \"normal\" gait cycle. This time, a `MuscleAnalysis` must be performed to obtain the fiber lengths of the muscles." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "osLz4Dbv-3PK" + }, + "source": [ + "First, let's load the motion files to obtain the initial and ending time. In both files, the initial and ending times are the same, so we obtain the values from just one of them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ktoXE8561QRJ" + }, + "outputs": [], + "source": [ + "# Load motion file and extract time.\n", + "tableTime = osim.TimeSeriesTable('normal_gait.mot')\n", + "x_time = tableTime.getIndependentColumn()\n", + "\n", + "# Get first and last time.\n", + "first_time = x_time[0]\n", + "last_time = x_time[len(x_time) - 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "91ktx1Ag3OY_" + }, + "source": [ + "Now, let's create a `MuscleAnalysis` and configure it. This analysis will be applied two times: one for the normal gait and another for the crouch gait." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dDKQQH5R3T5y" + }, + "outputs": [], + "source": [ + "# Define a MuscleAnalysis.\n", + "muscle_analysis = osim.MuscleAnalysis()\n", + "\n", + "# Set start and end times for the analysis.\n", + "muscle_analysis.setStartTime(first_time)\n", + "muscle_analysis.setEndTime(last_time)\n", + "\n", + "# Set the muscle of interest (semitendinosus on the right leg).\n", + "muscle_list = osim.ArrayStr()\n", + "muscle_list.append(\"semiten_r\")\n", + "muscle_analysis.setMuscles(muscle_list)\n", + "\n", + "# Configure the analysis.\n", + "muscle_analysis.setOn(True)\n", + "muscle_analysis.setStepInterval(1)\n", + "muscle_analysis.setInDegrees(True)\n", + "muscle_analysis.setComputeMoments(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ABbHd8br4JsA" + }, + "source": [ + "For the next step, we need to create an `AnalyzeTool` that, given a model file and a motion file, to perform the requested analysis. We have to create an `AnalyzeTool` for the normal gait and another one for the crouch gait. This may take a few seconds." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "AvG1N-t64WKs" + }, + "outputs": [], + "source": [ + "## Normal Gait.\n", + "\n", + "# Create an AnalyzeTool for normal gait.\n", + "analyze_tool_normal_gait = osim.AnalyzeTool()\n", + "analyze_tool_normal_gait.setName(\"Muscle_Analysis_Normal_Gait\")\n", + "\n", + "# Set the model file and motion file to analyze.\n", + "analyze_tool_normal_gait.setModelFilename(\"gait2392.osim\")\n", + "analyze_tool_normal_gait.setCoordinatesFileName(\"normal_gait.mot\")\n", + "\n", + "# Add the MuscleAnalysis to the AnalyzeTool.\n", + "analyze_tool_normal_gait.updAnalysisSet().cloneAndAppend(muscle_analysis)\n", + "\n", + "# Directory where results are stored.\n", + "analyze_tool_normal_gait.setResultsDir(\"MA_Normal_Gait_Results\")\n", + "\n", + "# Configure AnalyzeTool.\n", + "analyze_tool_normal_gait.setReplaceForceSet(False)\n", + "analyze_tool_normal_gait.setSolveForEquilibrium(True)\n", + "analyze_tool_normal_gait.setStartTime(first_time)\n", + "analyze_tool_normal_gait.setFinalTime(last_time)\n", + "\n", + "# Print configuration of the AnalyzeTool to an XML file.\n", + "analyze_tool_normal_gait.printToXML(\"Muscle_Analysis_Normal_Gait_AnalyzeTool_setup.xml\")\n", + "\n", + "# Load configuration and run the analyses. \n", + "analyze_tool_normal_gait = osim.AnalyzeTool(\"Muscle_Analysis_Normal_Gait_AnalyzeTool_setup.xml\", True)\n", + "result_normal_gait = analyze_tool_normal_gait.run()\n", + "\n", + "\n", + "## Crouch Gait.\n", + "\n", + "# Create an AnalyzeTool for crouch gait.\n", + "analyze_tool_crouch_gait = osim.AnalyzeTool()\n", + "analyze_tool_crouch_gait.setName(\"Muscle_Analysis_Crouch_Gait\")\n", + "\n", + "# Set the model file and motion file to analyze.\n", + "analyze_tool_crouch_gait.setModelFilename(\"gait2392.osim\")\n", + "analyze_tool_crouch_gait.setCoordinatesFileName(\"crouch_gait.mot\")\n", + "\n", + "# Add the Muscleanalysis to the AnalyzeTool.\n", + "analyze_tool_crouch_gait.updAnalysisSet().cloneAndAppend(muscle_analysis)\n", + "\n", + "# Directory where results are stored.\n", + "analyze_tool_crouch_gait.setResultsDir(\"MA_Crouch_Gait_Results\")\n", + "\n", + "# Configure AnalyzeTool.\n", + "analyze_tool_crouch_gait.setReplaceForceSet(False)\n", + "analyze_tool_crouch_gait.setSolveForEquilibrium(True)\n", + "analyze_tool_crouch_gait.setStartTime(first_time)\n", + "analyze_tool_crouch_gait.setFinalTime(last_time)\n", + "\n", + "# Print configuration of the AnalyzeTool to an XML file.\n", + "analyze_tool_crouch_gait.printToXML(\"Muscle_Analysis_Crouch_Gait_AnalyzeTool_setup.xml\")\n", + "\n", + "# Load configuration and run the analyses. \n", + "analyze_tool_crouch_gait = osim.AnalyzeTool(\"Muscle_Analysis_Crouch_Gait_AnalyzeTool_setup.xml\", True)\n", + "result_crouch_gait = analyze_tool_crouch_gait.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yikNNh5i5WNa" + }, + "source": [ + "Now that the analysis has been performed, you can extract the fiber length of the semitendinous muscle!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "F5UYk24r5cy7" + }, + "outputs": [], + "source": [ + "## Normal gait\n", + "\n", + "# Get results from file.\n", + "table_fiber_length_normal_gait = osim.TimeSeriesTable(\n", + " \"MA_Normal_Gait_Results/Muscle_Analysis_Normal_Gait_MuscleAnalysis_FiberLength.sto\")\n", + "\n", + "# Get values.\n", + "time_normal_gait = table_fiber_length_normal_gait.getIndependentColumn()\n", + "fiber_length_semitendinous_normal_gait = table_fiber_length_normal_gait.getDependentColumn(\"semiten_r\")\n", + "\n", + "\n", + "## Crouch gait\n", + "\n", + "# Get results from file.\n", + "table_fiber_length_crouch_gait = osim.TimeSeriesTable(\n", + " \"MA_Crouch_Gait_Results/Muscle_Analysis_Crouch_Gait_MuscleAnalysis_FiberLength.sto\")\n", + "\n", + "# Get values.\n", + "time_crouch_gait = table_fiber_length_crouch_gait.getIndependentColumn()\n", + "fiber_length_semitendinous_crouch_gait = table_fiber_length_crouch_gait.getDependentColumn(\"semiten_r\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9Sw39wFy6_lZ" + }, + "source": [ + "Let's create a plot to analyze the fiber length of the semitendinous muscle." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "goCMxbavur78" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Normal Gait vs. Crouch Gait')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(time_normal_gait, fiber_length_semitendinous_normal_gait.to_numpy(), label='normal_gait_semitendinous_fiber_length')\n", + "axs.plot(time_crouch_gait, fiber_length_semitendinous_crouch_gait.to_numpy(), label='crouch_gait_semitendinous_fiber_length')\n", + "axs.set_title('Fiber Length')\n", + "axs.set_xlabel('Time')\n", + "axs.set_ylabel('Fiber Length')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x4L_1mUV50_S" + }, + "source": [ + "**Questions: Hamstrings Length**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CFKPhBCN56ny" + }, + "source": [ + "**1.** Study the curves. Based on the plot, how do the peak hamstring lengths in normal and crouch gait compare? For this patient, would you recommend a hamstrings lengthening surgery? " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fCAAlC3i-E5J" + }, + "source": [ + "**2.** What are some limitations of your analysis?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i7rhPT2id6gg" + }, + "source": [ + "## 4.6. Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1XK9UgItWQMF" + }, + "source": [ + "In this tutorial, you have downloaded and explored a model of the lower extremity. Then, you have used this model to study lower limb kinematics using motion files from previous simulation results. Finally, you have performed a `MuscleAnalysis` to determine the fiber length of the semitendinous muscle to analyze possible underlying causes of crouch gait." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lLWgd8Noun6Y" + }, + "source": [ + "## 4.7. Useful Links\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m8M3dlT5xQcD" + }, + "source": [ + "> **OpenSim Website:** https://opensim.stanford.edu/\n", + ">\n", + "> **OpenSim Python Scripting:** https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53085346/Scripting+in+Python\n", + ">\n", + "> **OpenSim API Documentation:** https://simtk.org/api_docs/opensim/api_docs/\n", + "> \n", + "> **OpenSim Creator Website:** https://opensimcreator.com/\n", + "> \n", + "> **SimTK Website:** https://simtk.org/projects/opensim\n", + "> \n", + "> **Biomechanics of Movement Course Videos:** https://www.youtube.com/channel/UCDNGy0KKNLQ-ztcL5h2Z6zA" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rFR2Rf9tk_9A" + }, + "source": [ + "## 4.8 Acknowledgments\n", + "\n", + "Allison Arnold and Scott Delp with help from many others.\n", + "\n", + "Thanks to [OpenSimColab](https://simtk.org/projects/opencolab) project [[5]](https://doi.org/10.1080/10255842.2022.2104607) for creating the first OpenSim Conda package." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2rwqw1LFq6Lz" + }, + "source": [ + "## 4.9. References" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TpbyAlHvfh8M" + }, + "source": [ + "> [1]. Uchida, T. K., Delp, S. L., & Delp, D. (2021). **Biomechanics of movement: The science of sports, robotics, and rehabilitation.** *MIT Press*. https://mitpress.mit.edu/9780262044202/biomechanics-of-movement/\n", + ">\n", + "> [2]. Delp, S. L., Anderson, F. C., Arnold, A. S., Loan, P., Habib, A., John, C. T., Guendelman, E., & Thelen, D. G. (2007). **OpenSim: open-source software to create and analyze dynamic simulations of movement.** *IEEE Transactions on Bio-Medical Engineering*, 54(11), 1940–1950. https://doi.org/10.1109/TBME.2007.901024\n", + ">\n", + "> [3]. Delp, S. L., Loan, J. P., Hoy, M. G., Zajac, F. E., Topp, E. L., & Rosen, J. M. (1990). **An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures.** *IEEE Transactions on Bio-Medical Engineering*, 37(8), 757–767. https://doi.org/10.1109/10.102791\n", + "> \n", + "> [4]. Arnold, A. S., Liu, M. Q., Schwartz, M. H., Ounpuu, S., & Delp, S. L. (2006). **The role of estimating muscle-tendon lengths and velocities of the hamstrings in the evaluation and treatment of crouch gait.** *Gait & Posture*, 23(3), 273–281. https://doi.org/10.1016/j.gaitpost.2005.03.003\n", + ">\n", + "> [5] Mokhtarzadeh, H., Jiang, F., Zhao, S., & Malekipour, F. (2022). **OpenColab project: OpenSim in Google colaboratory to explore biomechanics on the web.** *Computer Methods in Biomechanics and Biomedical Engineering*, 1–9. https://doi.org/10.1080/10255842.2022.2104607" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "opensim", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Bindings/Python/tutorials/Tutorial 5 - Scaling, Inverse Kinematics, and Inverse Dynamics.ipynb b/Bindings/Python/tutorials/Tutorial 5 - Scaling, Inverse Kinematics, and Inverse Dynamics.ipynb new file mode 100644 index 0000000000..63e3feb44e --- /dev/null +++ b/Bindings/Python/tutorials/Tutorial 5 - Scaling, Inverse Kinematics, and Inverse Dynamics.ipynb @@ -0,0 +1,1126 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EhobipLVJg7S" + }, + "source": [ + "# 5. Scaling, Inverse Kinematics, and Inverse Dynamics\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SwX6QwGU0Jwj" + }, + "source": [ + "## 5.1. Objectives" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4p_3qlB60Np3" + }, + "source": [ + "**Purpose**\n", + "\n", + "The purpose of this tutorial is to demonstrate how [OpenSim](https://opensim.stanford.edu/) [[1]](https://doi.org/10.1109/TBME.2007.901024) can be used to scale a model, and then solve the Inverse Kinematics (IK) and Inverse Dynamics (ID) problems using experimental data. \n", + "\n", + "To diagnose movement disorders and study human movement, biomechanists frequently ask human subjects to perform movements in a motion capture laboratory and use computational tools to analyze these movements. A common step in analyzing a movement is to compute the joint angles and joint moments of the subject during movement. OpenSim has tools for computing these quantities:\n", + "\n", + "* **Inverse Kinematics (IK)** is used to compute joint angles.\n", + "* **Inverse Dynamics (ID)** is used to compute net joint reaction forces and net joint moments.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "urG9ZNhGHMLo" + }, + "source": [ + "\n", + "![Inverse Kinematics and Dynamics.png](data:image/png;base64,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)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6PpwC4C9HO1M" + }, + "source": [ + "\n", + "Inverse Kinematics computes the joint angles for a musculoskeletal model that best reproduce the motion of a subject. Inverse Dynamics then uses joint angles, angular velocities, and angular accelerations of the model, together with the experimental ground reaction forces and moments, to solve for the net reaction forces and net moments at each of the joints. The schematic above shows an overview of the inverse kinematics and inverse dynamics problems.\n", + "\n", + "In this tutorial, you will:\n", + "\n", + "* Become familiar with OpenSim's Scale, Inverse Kinematics and Inverse Dynamics tools.\n", + "* Solve an Inverse Kinematics and an Inverse Dynamics problem using experimental data.\n", + "* Investigate the dynamic inconsistencies that arise during Inverse Dynamics.\n", + "\n", + "**Format**\n", + "\n", + "Each section of the tutorial guides you through certain tools within OpenSim and asks you to answer a few questions. The questions can be answered based on information from OpenSim and basic knowledge of the human musculoskeletal system. As you complete each section of the tutorial, feel free to explore OpenSim and the lower extremity model further on your own.\n", + "\n", + "**Resources**\n", + "\n", + "All of the files necessary to complete this tutorial are available in the following Google Drive folder: https://drive.google.com/drive/folders/1txjRDKPETE1NpOwzv0pSdIiCoc8aIZTf?usp=sharing\n", + "\n", + "While you complete the tutorial, you will find cells that automatically download the required files.\n", + "\n", + "To obtain a link to one of the files in Google Drive, right-click over it and click on **Get Link**.\n", + "\n", + "**Local Installation**\n", + "\n", + "You may also run this notebook using a locally installed OpenSim `conda` package. Visit [the OpenSim python scripting Confluence page](https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53085346/Scripting+in+Python) for more information on how to install OpenSim in a local Python environment. You will also need to download the resources from Google Drive to the same directory as this notebook. Then, you may skip to Section 5.3 below." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ADllcxbsjTr8" + }, + "source": [ + "## 5.2. Set up Conda and OpenSim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mmmTuEI4HQJG" + }, + "source": [ + "First, set up the environment by executing the following cell (See [Tutorial 1: Introduction to OpenSim in Colab](https://colab.research.google.com/github/nickbianco/opensim-core/blob/python_examples/Bindings/Python/tutorials/Tutorial%201%20-%20Introduction%20to%20OpenSim.ipynb) for more information)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0w9LYQXx5TO4" + }, + "outputs": [], + "source": [ + "!pip install -q condacolab\n", + "import condacolab\n", + "condacolab.install()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vt5loGbdfKb8" + }, + "source": [ + "Don't worry if after executing the previous cell you get an error saying that your session has failed. This happens because `condacolab` needs to restart the session for the changes to take effect. Therefore, you have to execute the previous cell before executing any other cell.\n", + "\n", + "Now, execute the following cell to install the OpenSim `conda` package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vWf9ObZJ5TO5" + }, + "outputs": [], + "source": [ + "!conda install opensim-org::opensim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "51viyamRzzZK" + }, + "source": [ + "## 5.3. Scaling a Musculoskeletal Model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z9iDkg-QVVhm" + }, + "source": [ + "In this tutorial, you will usea generic musculoskeletal model with 23 degrees of freedom and actuated by 54 muscles, named _3DGaitModel2354_ [[2]](https://doi.org/10.1109/10.102791) [[3]](https://doi.org/10.1080/10255849908907988). Detailed information about the model can found on the [Gait2392 and 2354 Models](https://simtk-confluence.stanford.edu:8443/display/OpenSim/Gait+2392+and+2354+Models) OpenSim documentation page hosted on Confluence." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5s5Jso_ozzZN" + }, + "source": [ + "Subject-specific modeling involves the following steps:\n", + "* **Scaling** a generic musculoskeletal model to modify the body scale factors of the generic model so that it matches the anthropometry of a particular subject.\n", + "* **Registering** the markers placed on the model to match the locations on the subject.\n", + "\n", + "Scaling and registration have a significant effect on the solutions to the Inverse Kinematics and Inverse Dynamics problems, so it is crucial to perform these steps carefully." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-0nVeiJAXruu" + }, + "source": [ + "A detailed explanation of the Scale Tool can be found on the [Scaling page](https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53090000/Scaling) of the documentation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TIQp3TtGYMyQ" + }, + "source": [ + "**Download Files**\n", + "\n", + "First, let's download the model file (`gait2354_simbody.osim`). The file is stored in a Google Drive folder (See **Resources** in the Objectives section)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "EAb6JtzeYObU" + }, + "outputs": [], + "source": [ + "!gdown \"1roNo6te-wyNk-4UPqfmV7SmUfef4Bfq3&confirm=t\" # gait2354_simbody.osim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bjlOR8fym2Pl" + }, + "source": [ + "Load the model into OpenSim. To test that the file has been downloaded correctly, let's print its name." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oGTQE8Azh6p8" + }, + "outputs": [], + "source": [ + "import opensim as osim\n", + "\n", + "# Load the model.\n", + "gait2354 = osim.Model('gait2354_simbody.osim')\n", + "print(\"Name of the model:\", gait2354.getName())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-ooHO9m13zen" + }, + "source": [ + "To scale the model, we are going to use a configuration file (`gait2354_Setup_Scale.xml`). This Scale Setup file is an XML file that contains pre-configured settings to scale and register the generic gait2354 musculoskeletal model to the dimensions of a subject for which we have experimental data. A detailed explanation of the Scale Tool can be found on the [Scaling](https://simtk-confluence.stanford.edu:8443/display/OpenSim/Scaling) page of the documentation. The following cell downloads the file. The file is stored in a Google Drive folder (See **Resources** in the Objectives section)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1ojzW5C2iHki" + }, + "outputs": [], + "source": [ + "!gdown \"1c0P8cN1zUmVFJxqvcpHZEDacyAoa6lQy&confirm=t\" # gait2354_Setup_Scale.xml" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jg5Pu8k_7osp" + }, + "source": [ + "We also need to download the MarketSet file (`gait2354_Scale_MarkerSet.xml`), which defines the position of the markers used when capturing experimental data. The file is stored in a Google Drive folder (See **Resources** in the Objectives section)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "u2MecrQliXiK" + }, + "outputs": [], + "source": [ + "!gdown \"1VNDVGM-MKVrYs9w3aTPCLHOfwK-s2Nn8&confirm=t\" # gait2354_Scale_MarkerSet.xml" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wqFikw-zPau0" + }, + "source": [ + "Finally, we need a marker file (`subject01_static.trc`) which provides information about the position of the markers during the *static trial*, which is a trial in a known pose and not moving and is a typical trial used for the scaling step. In this case, we download a file with information of the user in a static pose. The file is stored in a Google Drive folder (See **Resources** in the Objectives section)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "doLIHrRKi1KU" + }, + "outputs": [], + "source": [ + "!gdown \"19-ouxBQN-XUOJ-K8lRe16DUs8fqaN1Yz&confirm=t\" # subject01_static.trc" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "k1bbjgNFS8h2" + }, + "source": [ + "**Create Scale Tool**\n", + "\n", + "The Scale Tool helps us scale the model. We can pass a configuration file as a parameter (`gait2354_Setup_Scale.xml`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "juWpas9abaEf" + }, + "outputs": [], + "source": [ + "# Create a ScaleTool object using the configuration file.\n", + "scale_tool = osim.ScaleTool('gait2354_Setup_Scale.xml')\n", + "\n", + "# Print some information of the config file to test everything is correct.\n", + "print(\"Name:\", scale_tool.getName())\n", + "print(\"Subject Mass:\", scale_tool.getSubjectMass())\n", + "print(\"Subject Height:\", scale_tool.getSubjectHeight())\n", + "print(\"Notes:\", scale_tool.getPropertyByName(\"notes\").toString())\n", + "print()\n", + "\n", + "# Get model marker file name.\n", + "generic_model_maker = scale_tool.getGenericModelMaker()\n", + "print(\"Marker Set File Name:\", generic_model_maker.getMarkerSetFileName())\n", + "print()\n", + "\n", + "# Get marker file name.\n", + "marker_placer = scale_tool.getMarkerPlacer()\n", + "print(\"Marker Placer File Name:\", marker_placer.getMarkerFileName())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6UoQfXjAJiGk" + }, + "source": [ + "**Perform Scaling**\n", + "\n", + "Finally, we can scale the model by running the scale tool. You can visualize the resulting model (`subject01_simbody.osim`) in [OpenSim GUI](https://simtk.org/frs/index.php?group_id=91)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ISVbNGw-J-6_" + }, + "outputs": [], + "source": [ + "# Run Scale Tool.\n", + "scale_tool.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QKEfjnyn5ldK" + }, + "source": [ + "**Questions:**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5-GGyw8X5ldQ" + }, + "source": [ + "1. What is the mass of the generic musculoskeletal model? What was the mass of the subject? _Hint: Look for it in the `gait2354_simbody.osim` and `gait2354_Setup_Scale.xml` files._" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kFTHo50B5ldQ" + }, + "source": [ + "2. What frequency was the experimental motion data captured?\n", + "_Hint: Look for it in the `subject01_static.trc` file._" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UHMJUvVO5ldQ" + }, + "source": [ + "3. Which body segments were scaled manually? _Hint: Look for it in the `gait2354_Setup_scale.xml` file._" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ad2kW88oKPaw" + }, + "source": [ + "## 5.4. Inverse Kinematics" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sXZXzssqVmUN" + }, + "source": [ + "Kinematics is the study of motion without considering the forces and moments that produce that motion [[4]](https://onlinelibrary.wiley.com/doi/book/10.1002/9780470549148). The purpose of Inverse Kinematics (IK) is to estimate the joint angles of a particular subject from experimental data. In this section, you will estimate a subject's joint angles during walking by performing an IK analysis using the subject scaled model and experimentally collected walking data. \n", + "\n", + "For each time step of recorded motion data, IK computes a set of joint angles that put the model in a configuration that \"best matches\" the experimental kinematics. OpenSim determines this \"best match\" by solving a weighted least squares optimization problem with the goal of minimizing marker error. Marker error is defined as the distance between an experimental marker and the corresponding model marker. Each marker has an associated weighting value, specifying how strongly that marker's error term should be minimized in the least squares problem. For each time step, the `InverseKinematicsTool` solves for a vector of generalized coordinates (e.g., joint angles), $q$, that minimizes the weighted sum of marker errors, which is expressed as:\n", + "\n", + "$\\underset{q}{\\text{min}}\\Bigg[ \\sum_{i \\in \\text{markers}}{w_i || x_i^{\\text{exp}} - x_i(q) ||^2}^{}\\Bigg] $\n", + "\n", + "where $q$ is the vector of generalized coordinates (e.g., joint angles), $x_i^{\\text{exp}}$ is the position of the experimental marker $i$, $x_i(q)$ is the position of the corresponding model marker $i$ (which depends on $q$), and $w_i$ is the weight associated with marker $i$. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1ATo0wsJX66B" + }, + "source": [ + "A detailed explanation of the `InverseKinematicsTool` can be found on the [Inverse Kinematics page](https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53090037/Inverse+Kinematics) of the documentation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e2kJm4w-YuMF" + }, + "source": [ + "**Download the files**\n", + "\n", + "First, let's download the setup file for the `InverseKinematicsTool`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AlCIWcl6qXd2" + }, + "source": [ + "The first file to download is the XML setup file (`subject01_Setup_IK.xml`) for the IK tool. The file is stored in a Google Drive folder (See **Resources** in the Objectives section)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PoPq74CGj05x" + }, + "outputs": [], + "source": [ + "!gdown \"1z1YGOTZlTCA4jpjRFbz0FlEaen1bETou&confirm=t\" # subject01_Setup_IK.xml" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Aw4ARIVvqhuR" + }, + "source": [ + "The second file is the marker file (`subject01_walk1.trc`), which provides information about the position of the markers during a motion trial. The file is stored in a Google Drive folder (See **Resources** in the Objectives section)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9dJOeVIqj_aX" + }, + "outputs": [], + "source": [ + "!gdown \"10TVRHRNJNy766Zbejjv05n3SKh0B80qD&confirm=t\" # subject01_walk1.trc" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G6wtd-OJaF6m" + }, + "source": [ + "**Create the `InverseKinematicsTool`**\n", + "\n", + "The `InverseKinematicsTool` helps us solve the Inverse Kinematics problem. We can pass a configuration file as a parameter (`subject01_Setup_IK.xml`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DaP2nPJraF6m" + }, + "outputs": [], + "source": [ + "# Create an IK object using the configuration file.\n", + "inverse_kinematics_tool = osim.InverseKinematicsTool('subject01_Setup_IK.xml')\n", + "\n", + "# Print some information of the config file to check that everything is correct.\n", + "print(\"Name:\", inverse_kinematics_tool.getName())\n", + "print(\"Model File:\", inverse_kinematics_tool.get_model_file())\n", + "print(\"Marker File:\", inverse_kinematics_tool.get_marker_file())\n", + "print(\"Accuracy:\", inverse_kinematics_tool.get_accuracy())\n", + "print(\"Time Range: [\", inverse_kinematics_tool.get_time_range(0), \",\", inverse_kinematics_tool.get_time_range(1), \"]\")\n", + "print(\"Constraint Weight:\", inverse_kinematics_tool.get_constraint_weight())\n", + "print()\n", + "\n", + "# Print weights information\n", + "print(\"Weights:\")\n", + "task_set = inverse_kinematics_tool.get_IKTaskSet()\n", + "for i in range(task_set.getSize()):\n", + " task = task_set.get(i)\n", + " print(task.getName())\n", + " print(task.getWeight())\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lm09IBIac8QE" + }, + "source": [ + "**Perform Inverse Kinematics**\n", + "\n", + "Finally, we run the IK tool. You can visualize the resulting motion file (`subject01_walk1_ik.mot`) by associating it to the scaled model (`subject01_simbody.osim`) in the [OpenSim GUI](https://simtk.org/frs/index.php?group_id=91)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "k6EIzdm9dEEx" + }, + "outputs": [], + "source": [ + "inverse_kinematics_tool.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GLp_exuTePfI" + }, + "source": [ + "**Visualize the marker errors**\n", + "\n", + "The output file `subject01_ik_marker_errors.sto` provides the markers errors associated with each frame of the generated motion. You can parse this file using a `TableProcessor` and visualize the error obtained per frame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "aiBr4M-wxrRP" + }, + "outputs": [], + "source": [ + "# Use the TableProcessor to read the motion file.\n", + "table = osim.TableProcessor(\"subject01_ik_marker_errors.sto\")\n", + "# Process the file.\n", + "tableErrors = table.process()\n", + "# Print labels for each column.\n", + "print(tableErrors.getColumnLabels())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-Ov2v2k9x5oe" + }, + "source": [ + "We will plot the total squared error, the RMS error for the markers, and the maximum error for the markers. In the next cell you will extract this information from the motion file.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Xhnx_ulsx6CS" + }, + "outputs": [], + "source": [ + "# Get columns we want to plot, and the independent column (Time).\n", + "total_squared_error = tableErrors.getDependentColumn('total_squared_error')\n", + "marker_error_RMS = tableErrors.getDependentColumn('marker_error_RMS')\n", + "marker_error_max = tableErrors.getDependentColumn('marker_error_max')\n", + "x_time = tableErrors.getIndependentColumn()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WfpRt8xMjhy3" + }, + "source": [ + "Now, we can plot the error." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "w4J1bJGpzC3U" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Marker Errors from Inverse Kinematics', fontsize=16)\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(x_time, total_squared_error.to_numpy(), label='total_squared_error')\n", + "axs.plot(x_time, marker_error_RMS.to_numpy(), label='marker_error_RMS')\n", + "axs.plot(x_time, marker_error_max.to_numpy(), label='marker_error_max')\n", + "axs.set_xlabel('Time (s)')\n", + "axs.set_ylabel('Marker Error (m)')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uJ25R72HJ9YX" + }, + "source": [ + "**Questions:**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_F4VGaDkmW-I" + }, + "source": [ + "4. Which markers have weighting values less than one? Why?\n", + "*Hint: Think about joints that have not been modeled.*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Oa7-QlzImfut" + }, + "source": [ + "5. What is the root-mean-squared (RMS) error of all the markers in the last frame of the motion? Include units. Does this seem reasonable? Explain." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hSO963Q1J7o9" + }, + "source": [ + "6. What was the value of the maximum marker error in the last frame? Include units. Which marker had this maximum error, and why?\n", + "*Hint: Think about the weighted least squares problem.*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SzMh1MS8QazO" + }, + "source": [ + "## 5.5. Inverse Dynamics" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QL2BMH6tQiVV" + }, + "source": [ + "Dynamics is the study of motion and the forces and moments that produce that motion [[4]](https://onlinelibrary.wiley.com/doi/book/10.1002/9780470549148). The purpose of Inverse Dynamics is to estimate the forces and moments that cause a particular motion, and its results can be used to infer how muscles are utilized in that motion. To determine these forces and moments, equations of motion for the system are solved iteratively [[5]](https://doi.org/10.1115/1.2834295). The equations of motion are derived using the kinematic description and mass properties of a musculoskeletal model. Then, using the joint angles from Inverse Kinematics and experimental ground reaction force data, the net reaction forces and net moments at each of the joints are calculated such that the dynamic equilibrium conditions and boundary conditions are satisfied [[5]](https://doi.org/10.1115/1.2834295). " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cR5SdHfdYH30" + }, + "source": [ + "A detailed explanation of the `InverseDynamicsTool` can be found on the [Inverse Dynamics page](https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53090074/Inverse+Dynamics) of the OpenSim documentation hosted on confluence." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wnJUuAW8RGpe" + }, + "source": [ + "**Download the files**\n", + "\n", + "First, let's download the configuration file (`subject01_Setup_InverseDynamics.xml`) for the `InverseDynamicsTool`. The file is stored in a Google Drive folder (See **Resources** in the Objectives section)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SDK4_QCkk0vo" + }, + "outputs": [], + "source": [ + "!gdown \"1aE_GIV0dNepL6oUGJalznsJQZDlSfcet&confirm=t\" # subject01_Setup_InverseDynamics.xml" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XlRizvvXrXtQ" + }, + "source": [ + "The second file is a external loads file (`subject01_walk1_grf.xml`), that contains the external loads applied to the model. The file is stored in a Google Drive folder (See **Resources** in the Objectives section)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "a3Ym-2KglEey" + }, + "outputs": [], + "source": [ + "!gdown \"1OZtWIpJ1UH2uViiEEeRluwfi54wsgIqG&confirm=t\" # subject01_walk1_grf.xml" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ePpe9dGXr_II" + }, + "source": [ + "The third file is a coordinates file (`subject01_walk1_grf.mot`), containing coordinate data. The file is stored in a Google Drive folder (See **Resources** in the Objectives section)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IoYI-ik3lUsq" + }, + "outputs": [], + "source": [ + "!gdown \"18L9Nj9ff-2evQkiXqD2KDZXu3DWZ30vh&confirm=t\" # subject01_walk1_grf.mot" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oHeUXkItSy6D" + }, + "source": [ + "**Create the `InverseDynamicsTool`**\n", + "\n", + "The `InverseDynamicsTool` helps us perform Inverse Dynamics. We can pass a configuration file as a parameter (`subject01_Setup_InverseDynamics.xml`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "r17DfremSy6E" + }, + "outputs": [], + "source": [ + "# Create an ID object using the configuration file.\n", + "inverse_dynamics_tool = osim.InverseDynamicsTool('subject01_Setup_InverseDynamics.xml')\n", + "\n", + "# Print some information of the config file to check that everything is correct.\n", + "print(\"Name:\", inverse_dynamics_tool.getName())\n", + "print(\"Model File:\", inverse_dynamics_tool.getModelFileName())\n", + "print(\"External Loads File:\", inverse_dynamics_tool.getExternalLoadsFileName())\n", + "print(\"Coordinates File:\", inverse_dynamics_tool.getCoordinatesFileName())\n", + "print(\"Time Range: [\", inverse_dynamics_tool.getStartTime(), \",\", inverse_dynamics_tool.getEndTime(), \"]\")\n", + "print(\"Lowpass Cutoff Frequency:\", inverse_dynamics_tool.getLowpassCutoffFrequency())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fHgMyZc2Y6JO" + }, + "source": [ + "**Run Inverse Dynamics**\n", + "\n", + "Finally, we can run Inverse Dynamics by running the `InverseDynamicsTool`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "VF3_KRo8Y6Jk" + }, + "outputs": [], + "source": [ + "inverse_dynamics_tool.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lUy_Vm8izR0f" + }, + "source": [ + "**Visualize the Inverse Dynamics results**\n", + "\n", + "After running the Inverse Dynamics Tool, a file named `inverse_dynamics.sto` is generated. This file contains the results of the Inverse Dynamics execution, and you can parse it using a `TimeSeriesTable` to visualize the results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "07K30qNozR0z" + }, + "outputs": [], + "source": [ + "# Load the inverse dynamics results into a TimeSeriesTable.\n", + "tableID = osim.TimeSeriesTable(\"ResultsInverseDynamics/inverse_dynamics.sto\")\n", + "\n", + "# Print labels for each column.\n", + "print(tableID.getColumnLabels())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bd3MDg8azR0z" + }, + "source": [ + "We are going to compare the moments of the ankle angle of both legs, so let's extract these quantities from the Inverse Dynamics results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hxG43RnlzR00" + }, + "outputs": [], + "source": [ + "# Get columns we want to represent, and the independent column (Time).\n", + "ankle_angle_r_moment = tableID.getDependentColumn('ankle_angle_r_moment')\n", + "ankle_angle_l_moment = tableID.getDependentColumn('ankle_angle_l_moment')\n", + "x_time = tableID.getIndependentColumn()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zLngF9DkzR00" + }, + "source": [ + "Now, we can plot the moments." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ql-_ZYAizjw0" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Ankle Angle Moment Comparison')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(x_time, ankle_angle_r_moment.to_numpy(), label='ankle_angle_r_moment')\n", + "axs.plot(x_time, ankle_angle_l_moment.to_numpy(), label='ankle_angle_l_moment')\n", + "axs.set_xlabel('Time (s)')\n", + "axs.set_ylabel('Moment (N-m)')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cHOJv4Mg1W_U" + }, + "source": [ + "In solving the Inverse Dynamics problem, both kinematic data and force plate data were used, making this an over-determined problem. In other words, the problem has more equations than unknowns (i.e., degrees of freedom). Due to errors in the experimental motion data and inaccuracies in the musculoskeletal model, it turns out that Newton's second law is violated, or $\\overrightarrow{F}_{\\text{exp}} \\neq m * \\overrightarrow{a}$ [[5]](https://doi.org/10.1115/1.2834295). One method to handle this inconsistency is to compute and apply residual forces and moments to a particular body segment in the model, such that Newton's second law becomes:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZFsf78-5t2L6" + }, + "source": [ + "$\\overrightarrow{F}_{\\text{exp}} + \\overrightarrow{F}_{\\text{residual}} = m * \\overrightarrow{a} $" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gFZfIUMp28Sx" + }, + "source": [ + "Note: An analogous equation relates the ground reaction moment, $\\overrightarrow{M}_{\\text{exp}}$ to the residual moment, $\\overrightarrow{M}_{\\text{residual}}$." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XdW9OdIA3MuC" + }, + "source": [ + "In this musculoskeletal model, the residuals are applied to the pelvis segment. To see the residuals from the inverse dynamics solution, plot `pelvis_tx_force`, `pelvis_ty_force`, and `pelvis_tz_force` versus `time`. Using this plot, answer question 9." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_Ryg0QjYzyfU" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Get columns we want to represent, and the independent column (Time).\n", + "pelvis_tx_force = tableID.getDependentColumn('pelvis_tx_force')\n", + "pelvis_ty_force = tableID.getDependentColumn('pelvis_ty_force')\n", + "pelvis_tz_force = tableID.getDependentColumn('pelvis_tz_force')\n", + "x_time = tableID.getIndependentColumn()\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Pelvis Force Residuals')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(x_time, pelvis_tx_force.to_numpy(), label='pelvis_tx_force')\n", + "axs.plot(x_time, pelvis_ty_force.to_numpy(), label='pelvis_ty_force')\n", + "axs.plot(x_time, pelvis_tz_force.to_numpy(), label='pelvis_tz_force')\n", + "axs.set_xlabel('Time')\n", + "axs.set_ylabel('Force')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oK6v_gocKWSh" + }, + "source": [ + "**Questions:**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cC88pJIY3TLV" + }, + "source": [ + "7. On your plot of the ankle moments, identify when heel strike, stance phase, toe off, and swing phase occur for each curve (i.e., left leg and right leg)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BLKse9bp3W7R" + }, + "source": [ + "8. Based on your plot and the angle convention for the ankle, give an explanation of what is happening at the ankle just before toe-off.\n", + "*Hint: It may be useful to visualize the motion in the OpenSim GUI and use the Coordinate sliders to understand the angle convention for the ankle.*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nquFUQIA4E-2" + }, + "source": [ + "9. What are the maximum magnitudes of the residual forces? Using the mass of the subject from Question 1, what fraction of body weight are the maximum residual forces?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "acVEWzHi8nRE" + }, + "source": [ + "While applying residual forces and moments makes the model's motion dynamically consistent with the external forces, this strategy is undesirable because the residuals can be large. More advanced strategies have been developed to deal with the problem of residuals and dynamic inconsistencies, such as least-squares optimization [[5]](https://doi.org/10.1115/1.2834295), the Residual Elimination Algorithm (REA) [[6]](https://doi.org/10.1016/j.jbiomech.2005.02.010), and the Residual Reduction Algorithm (RRA) [[7]](https://bcats.stanford.edu/previous_bcats/bcats06/pdf/BCATS_2006_abstract_book.pdf#page=31). OpenSim implements a Residual Reduction Algorithm as part of its workflow for generating muscle-actuated simulations [[7]](https://bcats.stanford.edu/previous_bcats/bcats06/pdf/BCATS_2006_abstract_book.pdf#page=31). A detailed explanation of the Residual Reduction Algorithm (RRA) can be found on the Residual Reduction Algorithm page of the documentation. For additional information on these strategies, please also see [[1]](https://doi.org/10.1080/10255842.2011.627560), [[5]](https://doi.org/10.1115/1.2834295), [[6]](https://doi.org/10.1016/j.jbiomech.2005.02.010) and [[7]](https://bcats.stanford.edu/previous_bcats/bcats06/pdf/BCATS_2006_abstract_book.pdf#page=31)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i7rhPT2id6gg" + }, + "source": [ + "## 5.6. Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1XK9UgItWQMF" + }, + "source": [ + "In this tutorial, you scaled a musculoskeletal model and used it to perform Inverse Kinematics and Inverse Dynamics. Then, you explored the results of each analyses." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lLWgd8Noun6Y" + }, + "source": [ + "## 5.7. Useful Links\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m8M3dlT5xQcD" + }, + "source": [ + "> **OpenSim Website:** https://opensim.stanford.edu/\n", + ">\n", + "> **OpenSim Python Scripting:** https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53085346/Scripting+in+Python\n", + ">\n", + "> **OpenSim API Documentation:** https://simtk.org/api_docs/opensim/api_docs/\n", + "> \n", + "> **OpenSim Creator Website:** https://opensimcreator.com/\n", + "> \n", + "> **SimTK Website:** https://simtk.org/projects/opensim\n", + "> \n", + "> **Biomechanics of Movement's Course:** https://www.youtube.com/channel/UCDNGy0KKNLQ-ztcL5h2Z6zA\n", + ">\n", + "> **Webinar on Scaling and Inverse Kinematics in OpenSim:** https://www.youtube.com/watch?v=ZG7wzvQC6eU" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yIZL9H9ygQGU" + }, + "source": [ + "## 5.8. Acknowledgments\n", + "\n", + "The experimental gait data were collected by Jill Higginson and Chand John in the Neuromuscular Biomechanics Lab at the University of Delaware [[8]](https://doi.org/10.1080/10255842.2011.627560). The data include marker trajectories and ground reaction forces for an adult male walking at a self-selected speed on an instrumented split-belt treadmill. Please note that the data distributed with OpenSim is from a different subject than the one described in the paper. Data collection protocols were the same for both subjects.\n", + "\n", + "Thanks to [OpenSimColab](https://simtk.org/projects/opencolab) project [[9]](https://doi.org/10.1080/10255842.2022.2104607) for creating the first OpenSim Conda package." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2rwqw1LFq6Lz" + }, + "source": [ + "## 5.9. References" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TpbyAlHvfh8M" + }, + "source": [ + "\n", + "> [1]. Delp, S. L., Anderson, F. C., Arnold, A. S., Loan, P., Habib, A., John, C. T., Guendelman, E., & Thelen, D. G. (2007). **OpenSim: open-source software to create and analyze dynamic simulations of movement.** *IEEE Transactions on Bio-Medical Engineering*, 54(11), 1940–1950. https://doi.org/10.1109/TBME.2007.901024\n", + "> \n", + "> [2]. Delp, S. L., Loan, J. P., Hoy, M. G., Zajac, F. E., Topp, E. L., & Rosen, J. M. (1990). **An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures.** *IEEE Transactions on Bio-Medical Engineering*, 37(8), 757–767. https://doi.org/10.1109/10.102791\n", + ">\n", + "> [3]. Anderson, F. C., & Pandy, M. G. (1999). **A dynamic optimization solution for vertical jumping in three dimensions.** *Computer Methods in Biomechanics and Biomedical Engineering*, 2(3), 201–231. https://doi.org/10.1080/10255849908907988\n", + ">\n", + "> [4]. Winter, D. A. (1990). **The biomechanics and motor control of human movement** (2a ed.). *John Wiley & Sons*. https://onlinelibrary.wiley.com/doi/book/10.1002/9780470549148\n", + ">\n", + "> [5]. Kuo, A. D. (1998). **A least-squares estimation approach to improving the precision of inverse dynamics computations.** *Journal of Biomechanical Engineering*, 120(1), 148–159. https://doi.org/10.1115/1.2834295\n", + ">\n", + "> [6]. Thelen, D. G., & Anderson, F. C. (2006). **Using computed muscle control to generate forward dynamic simulations of human walking from experimental data.** *Journal of Biomechanics*, 39(6), 1107–1115. https://doi.org/10.1016/j.jbiomech.2005.02.010\n", + ">\n", + "> [7]. John, C.T., Anderson, F.C., Guendelman, E., Arnold, A.S., Delp, S.L. (21st October 2006). **An algorithm for generating muscle-actuated simulations of long-duration movements.** *Biomedical Computation at Stanford (BCATS) Symposium*, Stanford University, Poster Presentation. https://bcats.stanford.edu/previous_bcats/bcats06/pdf/BCATS_2006_abstract_book.pdf#page=31\n", + ">\n", + "> [8]. John, C. T., Anderson, F. C., Higginson, J. S., & Delp, S. L. (2013). **Stabilisation of walking by intrinsic muscle properties revealed in a three-dimensional muscle-driven simulation.** *Computer Methods in Biomechanics and Biomedical Engineering*, 16(4), 451–462. https://doi.org/10.1080/10255842.2011.627560\n", + ">\n", + "> [9] Mokhtarzadeh, H., Jiang, F., Zhao, S., & Malekipour, F. (2022). **OpenColab project: OpenSim in Google colaboratory to explore biomechanics on the web.** *Computer Methods in Biomechanics and Biomedical Engineering*, 1–9. https://doi.org/10.1080/10255842.2022.2104607" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "opensim", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Bindings/Python/tutorials/Tutorial 6 - Static Optimization.ipynb b/Bindings/Python/tutorials/Tutorial 6 - Static Optimization.ipynb new file mode 100644 index 0000000000..dd56528ee2 --- /dev/null +++ b/Bindings/Python/tutorials/Tutorial 6 - Static Optimization.ipynb @@ -0,0 +1,2150 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JLgpKeJ4NYHz" + }, + "source": [ + "# 6. Static Optimization" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SwX6QwGU0Jwj" + }, + "source": [ + "## 6.1. Objectives" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4p_3qlB60Np3" + }, + "source": [ + "**Introduction**\n", + "\n", + "Static Optimization is a method for estimating muscle activations and muscle forces that satisfy the positions, velocities, accelerations, and external forces (e.g., ground reaction forces) of a motion. The technique is called \"static\" since calculations are performed at each time frame, without integrating the equations of motion between time steps. Static Optimization can be very fast and efficient because there is no integration, but it does ignore activation dynamics and tendon compliance. See [[1]](https://doi.org/10.1115/1.4029304) for more details regarding this and similar modeling and simulation choices and their pros and cons." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_cj6YClyLG_C" + }, + "source": [ + "\n", + "**Purpose**\n", + "\n", + "In this tutorial, you will learn how to perform a Static Optimization analysis on a single stride gait trial and assess gastrocnemius activation and force production in a simplified example model ([gait10dof18musc](https://simtk-confluence.stanford.edu:8443/display/OpenSim/Musculoskeletal+Models)).\n", + "\n", + "As with any analysis or simulation, the Static Optimization results' quality largely depends on the quality of the model, motion, and forces used as inputs. The model should have mass, anthropometry, and strength that represent the experimental participant; while the degrees of freedom and muscle geometry should be appropriate for the questions being asked (e.g., studying an upper body motion would require a more detailed upper extremity muscle set and skeletal geometry). The motion should contain smooth, realistic accelerations and all external forces during the motion (e.g., ground reaction forces) should be accurately measured and applied to the model. Additional forces (i.e., reserves and residuals) are often needed but should be small enough to not confound analysis. Any issues with your inputs will give you poor results or cause the tool to fail.\n", + "\n", + "In this tutorial, you will:\n", + "\n", + "* Get an initial Static Optimization analysis running without error messages or crashes.\n", + "* Improve the activation and force results by using improved motion data.\n", + "* Learn how to reduce actuation from reserve and residual actuators.\n", + "* Add passive forces and observe their effect on muscle activation.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G7_bQ5HTLJFl" + }, + "source": [ + "**Format**\n", + "\n", + "Each section of the tutorial guides you through certain tools within OpenSim and asks you to answer a few questions. The questions can be answered based on information from OpenSim and basic knowledge of the human musculoskeletal system. As you complete each section of the tutorial, feel free to explore OpenSim and the lower extremity model further on your own." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uoMuDaWxLMW1" + }, + "source": [ + "\n", + "**Before You Get Started**\n", + "\n", + "This tutorial begins in a workflow where the model is scaled, Inverse Kinematics run, and Residual Reduction Analysis performed.\n", + "\n", + "Before completing the tutorial, make sure you have reviewed the following reading from the User's Guide and completed the prerequisite tutorials.\n", + "\n", + "User Guide | Prerequisite Tutorials | Other Resources\n", + "--------------------|------------------------|------------------\n", + "[Static Optimization](https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53090088/Static+Optimization) | [Tutorial 1: Introduction to OpenSim](https://colab.research.google.com/github/nickbianco/opensim-core/blob/python_examples/Bindings/Python/tutorials/Tutorial%201%20-%20Introduction%20to%20OpenSim.ipynb) | [[1]](https://doi.org/10.1115/1.4029304) \n", + "[Inverse Kinematics](https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53090037/Inverse+Kinematics) | [Tutorial 2 : Creating and Simulating a Simple Arm Model ](https://colab.research.google.com/github/nickbianco/opensim-core/blob/python_examples/Bindings/Python/tutorials/Tutorial%202%20-%20Creating%20and%20Simulating%20a%20simple%20arm%20Model.ipynb) | [[2]](https://doi.org/10.1109/TBME.2007.901024) \n", + "[Residual Reduction Algorithm](https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53089669/Residual+Reduction+Algorithm) | [Tutorial 3: Loading and Modifying OpenSim Models](https://colab.research.google.com/github/nickbianco/opensim-core/blob/python_examples/Bindings/Python/tutorials/Tutorial%203%20-%20Loading%20and%20Modifying%20OpenSim%20Models.ipynb) | \n", + "[Inverse Dynamics](https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53090074/Inverse+Dynamics) | [Tutorial 4: Musculoskeletal Models, Motion Files, and MuscleAnalysis](https://colab.research.google.com/github/nickbianco/opensim-core/blob/python_examples/Bindings/Python/tutorials/Tutorial%204%20-%20Musculoskeletal%20models%2C%20Motion%20Files%20and%20MuscleAnalysis.ipynb) | | \n", + "[Scaling](https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53090000/Scaling) | [Tutorial 5: Scaling, Inverse Kinematics, and Inverse Dynamics](https://colab.research.google.com/github/nickbianco/opensim-core/blob/python_examples/Bindings/Python/tutorials/Tutorial%205%20-%20Scaling%2C%20Inverse%20Kinematics%2C%20and%20Inverse%20Dynamics.ipynb) | " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NeAzWgeVLOJL" + }, + "source": [ + "**Resources**\n", + "\n", + "All of the files necessary to complete this tutorial are available in the following Google Drive folder: https://drive.google.com/drive/folders/1XFkFE018DLXxPto6XdVRcY8Fl6hP3ecA?usp=sharing\n", + "\n", + "While you complete the tutorial, you will find cells that automatically download the required files.\n", + "\n", + "To obtain a link to one of the files in Google Drive, right-click over it and click on **Get Link**.\n", + "\n", + "**Local Installation**\n", + "\n", + "You may also run this notebook using a locally installed OpenSim `conda` package. Visit [the OpenSim python scripting Confluence page](https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53085346/Scripting+in+Python) for more information on how to install OpenSim in a local Python environment. You will also need to download the resources from Google Drive to the same directory as this notebook. Then, you may skip to Section 6.3 below." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ADllcxbsjTr8" + }, + "source": [ + "## 6.2. Setup `conda` and OpenSim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mmmTuEI4HQJG" + }, + "source": [ + "First, set up the environment by executing the following cell (See [Tutorial 1: Introduction to OpenSim](https://colab.research.google.com/github/nickbianco/opensim-core/blob/python_examples/Bindings/Python/tutorials/Tutorial%201%20-%20Introduction%20to%20OpenSim.ipynb) for more information)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0w9LYQXx5TO4" + }, + "outputs": [], + "source": [ + "!pip install -q condacolab\n", + "import condacolab\n", + "condacolab.install()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vt5loGbdfKb8" + }, + "source": [ + "Don't worry if after executing the previous cell you get an error saying that your session has failed. This happens because `condacolab` needs to restart the session for the changes to take effect. Therefore, you have to execute the previous cell before executing any other cell.\n", + "\n", + "Now, execute the following cell to install the OpenSim `conda` package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vWf9ObZJ5TO5" + }, + "outputs": [], + "source": [ + "!conda install opensim-org::opensim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "56GWGq3HSLNQ" + }, + "source": [ + "## 6.3. Study 1: Run Static Optimization" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2gJ1cEsvSUIB" + }, + "source": [ + "The objective of this study is to perform Static Optimization over the `gait10dof18musc` model, using results from IK as motion and a set of external forces applied to it. You don't have to worry about calculating the IK results and the external forces, since you will download them in the next subsection. Once Static Optimization has been calculated, we want to visualize the activation of the gastrocnemius muscle." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "337hUq4BSNHQ" + }, + "source": [ + "**Printing OpenSim logs**\n", + "\n", + "To visualize OpenSim's log information, we need to redirect it to either the console, or a file. In this case, we are going to do both." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mLyWh3uuZM1W" + }, + "source": [ + "To output OpenSim's log information to the console, we need to create a [LogSink](https://simtk.org/api_docs/opensim/api_docs/classOpenSim_1_1LogSink.html). A `LogSink` is an object that is able to report logging messages. We want the log messages to appear as python output, so we implement the class in a way that every message is printed, and we add the sink to OpenSim's [Logger](https://simtk.org/api_docs/opensim/api_docs/classOpenSim_1_1Logger.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lu_RSBjaNXkq" + }, + "source": [ + "To output OpenSim's log information to a file, we just need to call the function `addSinkFile()`, passing as an argument a name for the log file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kZO-sJ3sYGpG" + }, + "outputs": [], + "source": [ + "import opensim as osim\n", + "\n", + "class PythonOutputSink(osim.common.LogSink):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.msg = \"\"\n", + "\n", + " def flushImpl(self):\n", + " self.msg = \"\"\n", + "\n", + " def sinkImpl(self, str):\n", + " self.msg += str + \"\\n\";\n", + " print(self.msg)\n", + "\n", + "python_output_sink = PythonOutputSink()\n", + "\n", + "osim.Logger.addSink(python_output_sink)\n", + "osim.Logger.addFileSink(\"opensim-log.log\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e2kJm4w-YuMF" + }, + "source": [ + "**Download the files**\n", + "\n", + "First, let's download the model (`gait10dof18musc_simbody.osim`), the IK results (`subject01_walk_IK.mot`) and the external forces (`subject01_walk_grf.xml` and `subject01_walk_grf.mot`) files. The files are stored in a Google Drive folder (See **Resources** in the Objectives section)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "S_LJjt3YBDiZ" + }, + "outputs": [], + "source": [ + "!gdown \"1CB6-XNDMjNJ_Ca57AejgolT4iQTqVMGg&confirm=t\" # gait10dof18musc_simbody.osim\n", + "!gdown \"1sPgI8VzLvp6RC9KZMgAc4fA7S4CC6jQx&confirm=t\" # subject01_walk_IK.mot\n", + "!gdown \"1n7BN9rJv_OPW20HtFdiJr--2aqkg5Scq&confirm=t\" # subject01_walk_grf.xml\n", + "!gdown \"1wkMVlCN7ka4Bt-x8aBVj_c5oK9rH1tW6&confirm=t\" # subject01_walk_grf.mot" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bjlOR8fym2Pl" + }, + "source": [ + "Load the model into OpenSim. To check that the file has been downloaded correctly, let's print its name. Notice that we are getting more information printed to the console because of the Python `LogSink` that we've added. You can also see the logs in the `opensim-log.log` file that has been created.\n", + "\n", + "Don't worry about logged messages similar to \"Couldn't find file 'sacrum.vtp'\"; VTP files are only used for visualization and not needed for this example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oGTQE8Azh6p8" + }, + "outputs": [], + "source": [ + "# Load the model.\n", + "gait1018 = osim.Model('gait10dof18musc_simbody.osim')\n", + "print(\"Name of the model:\", gait1018.getName())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GZ-Wuh6ACWpN" + }, + "source": [ + "**Create Static Optimization Analysis Tool**\n", + "\n", + "\n", + "The first step to perform a Static Optimization Analysis is to create a Static Optimization object and configure it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4yiF1t4mCRrH" + }, + "outputs": [], + "source": [ + "# Define a StaticOptimization object.\n", + "so = osim.StaticOptimization()\n", + "\n", + "# Set start and end times for the analysis.\n", + "start_time = 0.3\n", + "end_time = 1.5\n", + "so.setStartTime(start_time)\n", + "so.setEndTime(end_time)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iVvH2t6sMc7t" + }, + "source": [ + "Then, we need to create an `AnalyzeTool`. We attach two files to the `AnalyzeTool`:\n", + "* The IK results file (`subject01_walk_IK.mot`).\n", + "* The external loads file (`subject01_walk_grf.xml`).\n", + "\n", + "The tool will step through each time frame of the input motion. At each time, it will update the model coordinates, external loads, and calculate the muscle forces and activations needed to generate the model's accelerations based on the input motion." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YxX1dIKYEneO" + }, + "outputs": [], + "source": [ + "# Create analyze tool for static optimization.\n", + "so_analyze_tool = osim.AnalyzeTool()\n", + "so_analyze_tool.setName(\"SO\")\n", + "\n", + "# Set model file, motion files and external load file names.\n", + "so_analyze_tool.setModelFilename(\"gait10dof18musc_simbody.osim\")\n", + "so_analyze_tool.setCoordinatesFileName(\"subject01_walk_IK.mot\")\n", + "so_analyze_tool.setExternalLoadsFileName(\"subject01_walk_grf.xml\")\n", + "\n", + "# Add analysis.\n", + "so_analyze_tool.updAnalysisSet().cloneAndAppend(so)\n", + "\n", + "# Configure analyze tool.\n", + "so_analyze_tool.setReplaceForceSet(False)\n", + "so_analyze_tool.setStartTime(start_time)\n", + "so_analyze_tool.setFinalTime(end_time)\n", + "\n", + "# Directory where results are stored.\n", + "so_analyze_tool.setResultsDir(\"SO_Results\")\n", + "\n", + "# Print configuration of analyze tool to a xml file.\n", + "so_analyze_tool.printToXML(\"SO_AnalyzeTool_setup.xml\")\n", + "\n", + "# Load configuration and run the analyses. \n", + "so_analyze_tool = osim.AnalyzeTool(\"SO_AnalyzeTool_setup.xml\", True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6u4xoyHdQXBq" + }, + "source": [ + "**Perform Static Optimization**\n", + "\n", + "With the Analysis Tool defined and configured, we can perform Static Optimization. This may take a few seconds." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "aUbfhgubQXhu" + }, + "outputs": [], + "source": [ + "# Run static optimization.\n", + "so_analyze_tool.run();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tpuoaR2SMl2l" + }, + "source": [ + "**Plot results**\n", + "\n", + "With the Static Optimization execution finished, now you can extract the activation of the gastrocnemius muscle from the generated result files and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "y4CjW5RNF3RR" + }, + "outputs": [], + "source": [ + "# Get results from file.\n", + "table_activation = osim.TimeSeriesTable(\n", + " \"SO_Results/SO_StaticOptimization_activation.sto\")\n", + "\n", + "# Get values.\n", + "time = table_activation.getIndependentColumn()\n", + "gastroc_activation = table_activation.getDependentColumn(\"gastroc_r\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "w4J1bJGpzC3U" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Gastrocnemius Activation')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(time, gastroc_activation.to_numpy(), label='Gastrocnemius (right)')\n", + "axs.set_xlabel('Time (s)')\n", + "axs.set_ylabel('Activation')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QGiGKzOdNIn1" + }, + "source": [ + "**Results Explanation**\n", + "\n", + "What exactly is happening? If you check the output of the Static Optimization execution (when you executed run on the AnalyzeTool), there is an error message saying **Optimizer Failed...**. This is informing you that the optimizer could not find an adequate solution at the time indicated. However, it is not crashing since the tool is trying to find adequate solutions in the following times. Remember that Static Optimization is trying to calculate activations and muscle forces that satisfy the model's coordinate accelerations calculated from the specified kinematics file. If the model's actuators cannot generate sufficient forces to match these accelerations, this \"Optimizer Failed...\" error will appear. The Static Optimization tool has produced a set of results to help with troubleshooting, but these outputs are not valid." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jkI_ssdCoZrM" + }, + "source": [ + "An under-actuated model is the product of muscles not being strong enough to generate the required forces and/or model coordinates not having any actuation (muscles or other actuators). In this particular model, there are no muscles that control lumbar flexion/extension, so all the other lower limb muscles are trying to control trunk motion, without much success. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WZhmZfCeobei" + }, + "source": [ + "One possible solution is to add extra muscles that cross the lumbar joint. However in this series of studies we are only interested in gastrocnemius activation during walking. Adding additional muscles will add extra complexity to the model that is likely not required for our analysis. Instead of lumbar muscles, we will add a torque actuator at the lumbar joint." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uEq4VwtQodXH" + }, + "source": [ + "We will also add reserve and residual actuators to the model. Reserve actuators can add extra actuation during portions of the gait cycle where muscles are not able to generate sufficient accelerations (e.g., during a spike in acceleration). Residual actuators are \"hand of God\" forces that account for (what should be small) discrepancies between the model, measured motions, and forces. In other words, these actuators ensure that Newton's 2$^{nd}$ law, $F=m*a$, is satisfied throughout the analysis. For more details, see _Section 3.1.3_ of [[1](https://doi.org/10.1115/1.4029304)]. Typically, these residual actuators are added to the model's body that is connected to ground (in this particular case, residual actuators will be added to the pelvis body)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-lsgJYW3Sd5W" + }, + "source": [ + "### 6.3.1. Adding Actuators to the Model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AA_lennaRyp0" + }, + "source": [ + "You will now add a set of additional residual actuators to fix the errors obtained in the previous section. This includes a lumbar actuator, reserve actuators at the other joints, and residual actuators at the pelvis. Once you have added the actuatiors, you will rerun the analysis to check if the problem is solved." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EZ41sHvFSFZq" + }, + "source": [ + "**Download the files**\n", + "\n", + "First, let's download the actuators file (`gait10dof18musc_Strong_actuators.xml`). The file is stored in a Google Drive folder (See **Resources** in the Objectives section)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9d2gSzuYFaS1" + }, + "outputs": [], + "source": [ + "!gdown \"1qYK7JULDucOl_GN3-74OetYJ9ckKcev6&confirm=t\" # gait10dof18musc_Strong_actuators.xml" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iJZg28MEkPoS" + }, + "source": [ + "**Create Static Optimization Analysis Tool**\n", + "\n", + "Now, let's add actuators to the Analyze Tool. You don't need to add the actuators manually since you have downloaded a file defining them (`gait10dof18musc_Strong_actuators.xml`). " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AZRgXMkMof3x" + }, + "source": [ + "In order to preserve previous results, we are going to change the name of the analysis tool and the name of the output folder." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Ziflsp26FAyW" + }, + "outputs": [], + "source": [ + "# Create analyze tool for static optimization.\n", + "so_strong_analyze_tool = osim.AnalyzeTool()\n", + "so_strong_analyze_tool.setName(\"SO_Strong\")\n", + "\n", + "# Add actuators file.\n", + "forceSet_files = osim.ArrayStr()\n", + "forceSet_files.append(\"gait10dof18musc_Strong_actuators.xml\")\n", + "so_strong_analyze_tool.setForceSetFiles(forceSet_files)\n", + "\n", + "# Set model file, motion files and external load file names.\n", + "so_strong_analyze_tool.setModelFilename(\"gait10dof18musc_simbody.osim\")\n", + "so_strong_analyze_tool.setCoordinatesFileName(\"subject01_walk_IK.mot\")\n", + "so_strong_analyze_tool.setExternalLoadsFileName(\"subject01_walk_grf.xml\")\n", + "\n", + "# Add analysis.\n", + "so_strong_analyze_tool.updAnalysisSet().cloneAndAppend(so)\n", + "\n", + "# Configure analyze tool.\n", + "so_strong_analyze_tool.setReplaceForceSet(False)\n", + "so_strong_analyze_tool.setStartTime(start_time)\n", + "so_strong_analyze_tool.setFinalTime(end_time)\n", + "\n", + "# Directory where results are stored.\n", + "so_strong_analyze_tool.setResultsDir(\"SO_Strong_Results\")\n", + "\n", + "# Print configuration of analyze tool to a xml file.\n", + "so_strong_analyze_tool.printToXML(\"SO_Strong_AnalyzeTool_setup.xml\")\n", + "\n", + "# Load configuration and run the analyses. \n", + "so_strong_analyze_tool = osim.AnalyzeTool(\"SO_Strong_AnalyzeTool_setup.xml\", True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "O_ZXXdlfTpE_" + }, + "source": [ + "**Perform Static Optimization**\n", + "\n", + "Finally, we can perform Static Optimization on the model with the actuators. Notice how this time the calculations are faster, solutions are found at each time point, and there are no errors during the execution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OuRQXLZPTpFA" + }, + "outputs": [], + "source": [ + "# Run static optimization.\n", + "so_strong_analyze_tool.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c0wrCHCkUB-5" + }, + "source": [ + "**Plot Results**\n", + "\n", + "First, plot the lumbar actuator reserves from the output file `Static_Optimization_Strong_StaticOptimization_force.sto`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "G5PXag0uGuAa" + }, + "outputs": [], + "source": [ + "# Get results from file.\n", + "table_forces = osim.TimeSeriesTable(\n", + " \"SO_Strong_Results/SO_Strong_StaticOptimization_force.sto\")\n", + "\n", + "# Get values.\n", + "time_force = table_forces.getIndependentColumn()\n", + "lumbar_extension_reserve = table_forces.getDependentColumn(\"lumbar_extension_reserve\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4Tu63Acz2wBQ" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Lumbar Reserve')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(time_force, lumbar_extension_reserve.to_numpy(), label='Lumbar Reserve')\n", + "axs.set_xlabel('Time')\n", + "axs.set_ylabel('Moment (Nm)')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fENfqQHlUSNo" + }, + "source": [ + "Now, let's compare the gastrocnemius muscle activation curves from the two analyses (with and without additional actuators)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ND4iTk6OJn6P" + }, + "outputs": [], + "source": [ + "# Get results from file.\n", + "table_activation = osim.TimeSeriesTable(\n", + " \"SO_Strong_Results/SO_Strong_StaticOptimization_activation.sto\")\n", + "\n", + "# Get values.\n", + "time_actuation = table_activation.getIndependentColumn()\n", + "gastroc_activation_actuation = table_activation.getDependentColumn(\"gastroc_r\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RA4njbtC27cj" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Gastrocnemius Activation')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(time, gastroc_activation.to_numpy(), label='Without Actuation')\n", + "axs.plot(time_actuation, gastroc_activation_actuation.to_numpy(), label='With Actuation')\n", + "axs.set_xlabel('Time (s)')\n", + "axs.set_ylabel('Activation')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IJlwI5csUbnj" + }, + "source": [ + "The previous figure shows that the gastrocnemius activation was significantly reduced. The reduction is due to the additional torques generated by the actuators added to the model." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AHQ0fSvqUhWj" + }, + "source": [ + "Plot the moments produced by the right ankle reserve actuator from the output file `Static_Optimization_Strong_StaticOptimization_force.sto`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "51pJOOBCJ402" + }, + "outputs": [], + "source": [ + "# Get results from file.\n", + "table_force = osim.TimeSeriesTable(\n", + " \"SO_Strong_Results/SO_Strong_StaticOptimization_force.sto\")\n", + "\n", + "# Get values.\n", + "time_force = table_force.getIndependentColumn()\n", + "ankle_angle_r_reserve_force = table_force.getDependentColumn(\"ankle_angle_r_reserve\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fvVWUXMu3Jge" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Ankle Reserve Moment')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(time_actuation, ankle_angle_r_reserve_force.to_numpy(), label='Ankle Reserve')\n", + "axs.set_xlabel('Time')\n", + "axs.set_ylabel('Moment (Nm)')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9bKfj79eUqhu" + }, + "source": [ + "Finally, plot the forces `Fx`, `Fy`, and moment `Mz` residual actuator forces." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "mqilA22pKe_n" + }, + "outputs": [], + "source": [ + "# Get results from file.\n", + "table_force = osim.TimeSeriesTable(\n", + " \"SO_Strong_Results/SO_Strong_StaticOptimization_force.sto\")\n", + "\n", + "# Get values.\n", + "time_force = table_force.getIndependentColumn()\n", + "fx = table_force.getDependentColumn(\"FX\")\n", + "fy = table_force.getDependentColumn(\"FY\")\n", + "mz = table_force.getDependentColumn(\"MZ\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "J65OKGUI3UnJ" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Residual Forces and Moment')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(time_force, fx.to_numpy(), label='FX')\n", + "axs.plot(time_force, fy.to_numpy(), label='FY')\n", + "axs.plot(time_force, mz.to_numpy(), label='MZ')\n", + "axs.set_xlabel('Time')\n", + "axs.set_ylabel('Forces (N; FX, FY) & Moment (Nm; MZ)')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ppWz47WEUxt6" + }, + "source": [ + "**Results Explanation:**\n", + "\n", + "We now have a working Static Optimization analysis. A functioning analysis does not mean that the results are valid; rather, we have a place to start iterating from to improve the output. In the following studies, we will gradually improve the quality of the inputs to get a better result. Review the questions below to better understand the problems with the current set of results." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R5LoTvT-ipdJ" + }, + "source": [ + "**Questions:**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "okZLnDQ-mTuG" + }, + "source": [ + "* The activation and force results are very noisy; why do you think this is? We will learn how to fix this in Study 2." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QVA_u_pTmVlz" + }, + "source": [ + "* The Mz contribution is between ±50 N-m. What is the contribution of the residual actuator force for Fx, and Fy? How large are these residual actuator forces in comparison to the peak ground reaction forces measured doing the motion? What improvements could you make to any of the inputs (model, kinematics, external forces) to reduce the size of the residual forces? Study 2 will address these residual results. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fpknvfWjmXPi" + }, + "source": [ + "* The peak ankle extension moment from Inverse Dynamics was 120 N-m. How does this peak ankle moment compare to the peak moment generated by the ankle reserve actuator (e.g., as a percentage)? Do you think the reserve actuator peak should be this large? How might we address this issue? We will learn how to fix this in Study 3." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J6_F0oT8WlVA" + }, + "source": [ + "## 6.4. Study 2: Improving the input kinematics" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pgmwSu383oCu" + }, + "source": [ + "The aim of Study 2 is to assess how improving the input kinematics affects muscle activations and forces calculated by Static Optimization. Static Optimization is trying to compute muscle forces that generate the accelerations seen in the input kinematics. If these accelerations are noisy, there will be noise in the muscle activations and forces. In Study 1, we saw noisy activations and forces that are most likely the result of noisy motion data. We will investigate two methods for improving the motion data:\n", + "* Filtering the input kinematics.\n", + "* Using the Residual Reduction Algorithm (RRA) to generate a dynamically consistent motion with consistent accelerations" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VG0ylYq_j8Ok" + }, + "source": [ + "Filtering the kinematics will spline-fit the motion data and filter the coordinate positions, reducing noise in the accelerations. If you use IK results as inputs to Static Optimization, you should always filter them, either externally (e.g., with Matlab or Python) or by using the OpenSim filters." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oWf-l1v0j-VF" + }, + "source": [ + "Since RRA uses a forward dynamics simulation (i.e., the model's equations of motion are integrated forward in time), the output kinematics will be consistent accelerations that are great to use in Static Optimization. If you are using RRA results, you don't need to do any additional filtering. Using RRA also has the added benefit of generating a more \"dynamically consistent\" model and set of kinematics and forces. We are not going to go in depth into RRA in this particular tutorial so if you are unfamiliar with RRA please refer to the [User's Guide](https://simtk-confluence.stanford.edu:8443/display/OpenSim/Residual+Reduction+Algorithm)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UDysgMhAk9f6" + }, + "source": [ + "### 6.4.1. Filter the motion using a low pass filter" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7IZDMDJLkp6N" + }, + "source": [ + "**Create Static Optimization Analysis Tool**\n", + "\n", + "Let's create a Static Optimization Tool, but this time we have to append the reserve and residal actuators from the beginning, filter the motion using 6Hz cut-off, and change the output directory name, so results are not overwritten." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "b54SwBByXD-7" + }, + "outputs": [], + "source": [ + "# Create analyze tool for static optimization.\n", + "so_strong_filter_analyze_tool = osim.AnalyzeTool()\n", + "so_strong_filter_analyze_tool.setName(\"SO_Strong_Filter\")\n", + "\n", + "# Add actuators file.\n", + "forceSet_files = osim.ArrayStr()\n", + "forceSet_files.append(\"gait10dof18musc_Strong_actuators.xml\")\n", + "so_strong_filter_analyze_tool.setForceSetFiles(forceSet_files)\n", + "\n", + "# Set model file, motion files and external load file names.\n", + "so_strong_filter_analyze_tool.setModelFilename(\"gait10dof18musc_simbody.osim\")\n", + "so_strong_filter_analyze_tool.setCoordinatesFileName(\"subject01_walk_IK.mot\")\n", + "so_strong_filter_analyze_tool.setExternalLoadsFileName(\"subject01_walk_grf.xml\")\n", + "\n", + "# Change low pass frequency to filter the model.\n", + "so_strong_filter_analyze_tool.setLowpassCutoffFrequency(6)\n", + "\n", + "# Add analysis.\n", + "so_strong_filter_analyze_tool.updAnalysisSet().cloneAndAppend(so)\n", + "\n", + "# Configure analyze tool.\n", + "so_strong_filter_analyze_tool.setReplaceForceSet(False)\n", + "so_strong_filter_analyze_tool.setStartTime(start_time)\n", + "so_strong_filter_analyze_tool.setFinalTime(end_time)\n", + "\n", + "# Directory where results are stored.\n", + "so_strong_filter_analyze_tool.setResultsDir(\"SO_Strong_Filter_Results\")\n", + "\n", + "# Print configuration of analyze tool to a xml file.\n", + "so_strong_filter_analyze_tool.printToXML(\"SO_Strong_Filter_AnalyzeTool_setup.xml\")\n", + "\n", + "# Load configuration and run the analyses. \n", + "so_strong_filter_analyze_tool = osim.AnalyzeTool(\"SO_Strong_Filter_AnalyzeTool_setup.xml\", True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZU6sest-lTUH" + }, + "source": [ + "**Perform Static Optimization**\n", + "\n", + "We can perform Static Optimization on the model with the filtered motion." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DMyIhe-0XQml" + }, + "outputs": [], + "source": [ + "# Run static optimization.\n", + "so_strong_filter_analyze_tool.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xZBT-ScKlesI" + }, + "source": [ + "### 6.4.2. Smoothing the motion using RRA" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ikki8Olol3J8" + }, + "source": [ + "**Download the files**\n", + "\n", + "First, let's download the RRA motion file (`subject_adjusted_Kinematics_q.sto`). The file is stored in a Google Drive folder (See **Resources** in the Objectives section)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "H6eAcbj6jxkV" + }, + "outputs": [], + "source": [ + "!gdown \"1XpgUlSGV6T7SKRnCpcIomg5aZ4INreI2&confirm=t\" # subject_adjusted_Kinematics_q.sto" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_xBCmYBZmK_H" + }, + "source": [ + "**Create a Static Optimization Analysis Tool**\n", + "\n", + "Let's create a Static Optimization Tool, but this time we have to use the motion file kinematics generated by RRA (`subject_adjusted_Kinematics_q.sto`). Remember we are using RRA results so the kinematics are smoother. This time, do not apply a low pass filter, and change the step interval to 10. Again, change the output folder for the file so we don't overwrite previous results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "i___D9dylXCL" + }, + "outputs": [], + "source": [ + "# Define a StaticOptimization object.\n", + "so_rra = osim.StaticOptimization()\n", + "\n", + "# Set start and end times for the analysis.\n", + "start_time = 0.3\n", + "end_time = 1.5\n", + "so_rra.setStartTime(start_time)\n", + "so_rra.setEndTime(end_time)\n", + "\n", + "# Step interval.\n", + "so_rra.setStepInterval(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DTyCVAUHi8L3" + }, + "outputs": [], + "source": [ + "# Create analyze tool for static optimization.\n", + "so_strong_RRA_analyze_tool = osim.AnalyzeTool()\n", + "so_strong_RRA_analyze_tool.setName(\"SO_Strong_RRA\")\n", + "\n", + "# Add actuators file.\n", + "forceSet_files = osim.ArrayStr()\n", + "forceSet_files.append(\"gait10dof18musc_Strong_actuators.xml\")\n", + "so_strong_RRA_analyze_tool.setForceSetFiles(forceSet_files)\n", + "\n", + "# Set model file, motion files and external load file names.\n", + "so_strong_RRA_analyze_tool.setModelFilename(\"gait10dof18musc_simbody.osim\")\n", + "so_strong_RRA_analyze_tool.setCoordinatesFileName(\"subject_adjusted_Kinematics_q.sto\")\n", + "so_strong_RRA_analyze_tool.setExternalLoadsFileName(\"subject01_walk_grf.xml\")\n", + "\n", + "# Add analysis.\n", + "so_strong_RRA_analyze_tool.updAnalysisSet().cloneAndAppend(so_rra)\n", + "\n", + "# Configure analyze tool.\n", + "so_strong_RRA_analyze_tool.setReplaceForceSet(False)\n", + "so_strong_RRA_analyze_tool.setStartTime(start_time)\n", + "so_strong_RRA_analyze_tool.setFinalTime(end_time)\n", + "\n", + "# Directory where results are stored.\n", + "so_strong_RRA_analyze_tool.setResultsDir(\"SO_Strong_RRA_Results\")\n", + "\n", + "# Print configuration of analyze tool to a xml file.\n", + "so_strong_RRA_analyze_tool.printToXML(\"SO_Strong_RRA_AnalyzeTool_setup.xml\")\n", + "\n", + "# Load configuration and run the analyses. \n", + "so_strong_RRA_analyze_tool = osim.AnalyzeTool(\"SO_Strong_RRA_AnalyzeTool_setup.xml\", True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dKbZ-zO2myqS" + }, + "source": [ + "**Perform Static Optimization**\n", + "\n", + "We can now perform Static Optimization on the model with the smoothened motion." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "J1qSYStBjn1_" + }, + "outputs": [], + "source": [ + "# Run static optimization.\n", + "so_strong_RRA_analyze_tool.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Gd5hHheSm5zn" + }, + "source": [ + "### 6.4.3. Comparing results" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uj4UPhhrlvOg" + }, + "source": [ + "You should now have four analysis results in separate folders\n", + "\n", + "* **1. SO_Results**: Static Optimization, no extra actuation (residuals and reserves), unfiltered IK motion.\n", + "* **2. SO_Strong_Results**: Static Optimization, with extra actuation, unfiltered IK motion.\n", + "* **3. SO_Strong_Filter_Results**: Static Optimization, with extra actuation, filtered IK motion.\n", + "* **4. SO_Strong_RRA_Results**: Static Optimization, with extra actuation, RRA motion." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hOZztbjRmHJs" + }, + "source": [ + "**Plot Results**\n", + "\n", + "Generate figures that compare the gastrocnemius activations for the three different kinematics inputs (**2. unfiltered**, **3. filtered**, and **4. RRA**) where reserve and residual actuators were included (i.e., skip case 1)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6VZffDUjmMj0" + }, + "outputs": [], + "source": [ + "# Get results from file.\n", + "table_activation_SO_Strong = osim.TimeSeriesTable(\n", + " \"SO_Strong_Results/SO_Strong_StaticOptimization_activation.sto\")\n", + "\n", + "# Get values.\n", + "time_SO_Strong = table_activation_SO_Strong.getIndependentColumn()\n", + "gastrocnemius_muscle_activation_strong = table_activation_SO_Strong.getDependentColumn(\"gastroc_r\")\n", + "\n", + "# Get results from file.\n", + "table_activation_SO_Strong_Filter = osim.TimeSeriesTable(\n", + " \"SO_Strong_Filter_Results/SO_Strong_Filter_StaticOptimization_activation.sto\")\n", + "\n", + "# Get values.\n", + "time_SO_Strong_Filter = table_activation_SO_Strong_Filter.getIndependentColumn()\n", + "gastrocnemius_muscle_activation_strong_filtered = table_activation_SO_Strong_Filter.getDependentColumn(\"gastroc_r\")\n", + "\n", + "# Get results from file.\n", + "table_activation_SO_Strong_RRA = osim.TimeSeriesTable(\n", + " \"SO_Strong_RRA_Results/SO_Strong_RRA_StaticOptimization_activation.sto\")\n", + "\n", + "# Get values.\n", + "time_SO_Strong_RRA = table_activation_SO_Strong_RRA.getIndependentColumn()\n", + "gastrocnemius_muscle_activation_strong_rra = table_activation_SO_Strong_RRA.getDependentColumn(\"gastroc_r\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zpbV3yWj3_kH" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Gastrocnemius Activation')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(time_SO_Strong, gastrocnemius_muscle_activation_strong.to_numpy(), label='Unfiltered')\n", + "axs.plot(time_SO_Strong_Filter, gastrocnemius_muscle_activation_strong_filtered.to_numpy(), label='Filtered')\n", + "axs.plot(time_SO_Strong_RRA, gastrocnemius_muscle_activation_strong_rra.to_numpy(), label='RRA')\n", + "axs.set_xlabel('Time (s)')\n", + "axs.set_ylabel('Activation')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xUrKLshWnzsp" + }, + "source": [ + "Plot the `Fx` residual for the three kinematics inputs (**2. unfiltered**, **3. filtered**, and **4. RRA**)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NINOvRDwn0hD" + }, + "outputs": [], + "source": [ + "# Get results from file.\n", + "table_forces_SO_Strong = osim.TimeSeriesTable(\n", + " \"SO_Strong_Results/SO_Strong_StaticOptimization_force.sto\")\n", + "\n", + "# Get values.\n", + "time_SO_Strong = table_forces_SO_Strong.getIndependentColumn()\n", + "fx_strong = table_forces_SO_Strong.getDependentColumn(\"FX\")\n", + "\n", + "# Get results from file.\n", + "table_forces_SO_Strong_Filter = osim.TimeSeriesTable(\n", + " \"SO_Strong_Filter_Results/SO_Strong_Filter_StaticOptimization_force.sto\")\n", + "\n", + "# Get values.\n", + "time_SO_Strong_Filter = table_forces_SO_Strong_Filter.getIndependentColumn()\n", + "fx_strong_filtered = table_forces_SO_Strong_Filter.getDependentColumn(\"FX\")\n", + "\n", + "# Get results from file.\n", + "table_forces_SO_Strong_RRA = osim.TimeSeriesTable(\n", + " \"SO_Strong_RRA_Results/SO_Strong_RRA_StaticOptimization_force.sto\")\n", + "\n", + "# Get values.\n", + "time_SO_Strong_RRA = table_forces_SO_Strong_RRA.getIndependentColumn()\n", + "fx_strong_rra = table_forces_SO_Strong_RRA.getDependentColumn(\"FX\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wJ8l3LeY4TgY" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Fx Residual Force')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(time_SO_Strong, fx_strong.to_numpy(), label='Unfiltered')\n", + "axs.plot(time_SO_Strong_Filter, fx_strong_filtered.to_numpy(), label='Filtered')\n", + "axs.plot(time_SO_Strong_RRA, fx_strong_rra.to_numpy(), label='RRA')\n", + "axs.set_xlabel('Time (s)')\n", + "axs.set_ylabel('Force (N)')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n_EcxYFgoQeV" + }, + "source": [ + "**Questions**:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wy2agd2_npKU" + }, + "source": [ + "* Describe the changes in muscle activation for the three different motion inputs. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3R2D3eWEnjxv" + }, + "source": [ + "* Why did the residual force reduce from the Unfiltered to Filtered condition? Why did the residual force reduce further when using the model and motion from RRA?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Rc57Z-AdnlJZ" + }, + "source": [ + "* Which kinematic input should be used for the Static Optimization analysis? Explain your reason(s)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bJ7pb8YEnnJZ" + }, + "source": [ + "* Optional: Explore some of the other forces and activation results from the analysis." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-4q6_ZTFofwj" + }, + "source": [ + "## 6.5. Study 3: Tuning the reserve and residual actuators" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NXZyJcBtpDdU" + }, + "source": [ + "In Study 2, we added actuators to assist each coordinate of the model. However, we saw that these reserve actuators contributed a significant amount of the total joint torque that we would prefer the muscles to provide. In this study, you will reduce the strength of the reserve actuators and observe the resulting changes in reserve actuator torques and gastrocnemius muscle activation and forces. \n", + "\n", + "The current actuators file has optimal forces of $100 N$ for each force actuator (i.e., residuals $FX$ and $FY$) and optimal forces of $100 Nm$ for each torque actuator (i.e., residual $MZ$, and reserve coordinate actuators). When trying to minimize the sum of activations squared, the optimizer can use small activation values to these force and torque actuators to generate large forces or torques. For example, with an optimal torque of $100 Nm$, an activation of $0.5$ will generate a torque of $50 Nm$. If, instead, the optimal force were $1 Nm$, the optimizer must now use very high activations to produce any meaningful torques. In this latter case (optimal torque = 1), an activation of $0.5$ would only generate $0.5 Nm$, and an activation of $50$ is necessary to generate $50 Nm$ of torque. Thus, when the optimal force is very low ($~1 N$ or $1 Nm$) the optimizer can still choose to use the reserve actuators when needed, but the cost is so high relative to the muscles that the optimizer will preferentially use muscles to generate joint torques over the reserve actuators." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n5P-y18ssZQ3" + }, + "source": [ + "**Edit Actuators File**\n", + "\n", + "Edit the actuators file and change each actuator's optimal force from 100 to 1. You can do this programatically by loading the XML file and modifying the values of every element with tag `optimal_force`. The following function does this for you and saves the modified file as `gait10dof18musc_Actuators_Normal.xml`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-jyIrh2IvERk" + }, + "outputs": [], + "source": [ + "from xml.dom import minidom\n", + "\n", + "# Parse the xml file.\n", + "file = minidom.parse('gait10dof18musc_Strong_actuators.xml')\n", + "\n", + "# Get all elements with tag name 'optimal_force'.\n", + "optimal_forces = file.getElementsByTagName(\"optimal_force\")\n", + "\n", + "# Set all optimal forces to 1.\n", + "for optimal_force in optimal_forces:\n", + " optimal_force.firstChild.nodeValue = 1\n", + "\n", + "xml_str = file.toprettyxml(indent =\"\\t\", newl='\\r') \n", + "\n", + "with open(\"gait10dof18musc_Actuators_Normal.xml\", \"w\") as f:\n", + " f.write(xml_str) " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Sa8dYkuRsy5M" + }, + "source": [ + "**Create Static Optimization Analysis Tool**\n", + "\n", + "Perform a new Static Optimization Analysis, but this time use the new Additional Forces Set file (`gait10dof18musc_Actuators_Normal.xml`). Save the results in a different folder so previous results are not overwritten." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wETSKSk9xIte" + }, + "outputs": [], + "source": [ + "# Create analyze tool for static optimization.\n", + "so_normal_RRA_analyze_tool = osim.AnalyzeTool()\n", + "so_normal_RRA_analyze_tool.setName(\"SO_Normal_RRA\")\n", + "\n", + "# Add actuators file.\n", + "forceSet_files = osim.ArrayStr()\n", + "forceSet_files.append(\"gait10dof18musc_Actuators_Normal.xml\")\n", + "so_normal_RRA_analyze_tool.setForceSetFiles(forceSet_files)\n", + "\n", + "# Set model file, motion files and external load file names.\n", + "so_normal_RRA_analyze_tool.setModelFilename(\"gait10dof18musc_simbody.osim\")\n", + "so_normal_RRA_analyze_tool.setCoordinatesFileName(\"subject_adjusted_Kinematics_q.sto\")\n", + "so_normal_RRA_analyze_tool.setExternalLoadsFileName(\"subject01_walk_grf.xml\")\n", + "\n", + "# Add analysis.\n", + "so_normal_RRA_analyze_tool.updAnalysisSet().cloneAndAppend(so_rra)\n", + "\n", + "# Configure analyze tool.\n", + "so_normal_RRA_analyze_tool.setReplaceForceSet(False)\n", + "so_normal_RRA_analyze_tool.setStartTime(start_time)\n", + "so_normal_RRA_analyze_tool.setFinalTime(end_time)\n", + "\n", + "# Directory where results are stored.\n", + "so_normal_RRA_analyze_tool.setResultsDir(\"SO_Normal_RRA_Results\")\n", + "\n", + "# Print configuration of analyze tool to a xml file.\n", + "so_normal_RRA_analyze_tool.printToXML(\"SO_Normal_RRA_AnalyzeTool_setup.xml\")\n", + "\n", + "# Load configuration and run the analyses. \n", + "so_normal_RRA_analyze_tool = osim.AnalyzeTool(\"SO_Normal_RRA_AnalyzeTool_setup.xml\", True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ViLGeIUsuLcQ" + }, + "source": [ + "**Perform Static Optimization**\n", + "\n", + "We can now perform Static Optimization with the residual and reserve actuators each with an optimal force of 1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "esvOnBh4xsGN" + }, + "outputs": [], + "source": [ + "# Run static optimization.\n", + "so_normal_RRA_analyze_tool.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xm4GhhLVuGos" + }, + "source": [ + "**Plot Results**\n", + "\n", + "Once the analysis has finished, plot the gastrocnemius activation when using the strong actuators (optimal force of 100) vs the normal actuators (optimal force of 1)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1J4KRZnux6Xp" + }, + "outputs": [], + "source": [ + "# Get results from file.\n", + "table_activation_SO_Strong_RRA = osim.TimeSeriesTable(\n", + " \"SO_Strong_RRA_Results/SO_Strong_RRA_StaticOptimization_activation.sto\")\n", + "\n", + "# Get values.\n", + "time_SO_Strong_RRA = table_activation_SO_Strong_RRA.getIndependentColumn()\n", + "gastrocnemius_muscle_activation_strong_rra = table_activation_SO_Strong_RRA.getDependentColumn(\"gastroc_r\")\n", + "\n", + "# Get results from file.\n", + "table_activation_SO_Normal_RRA = osim.TimeSeriesTable(\n", + " \"SO_Normal_RRA_Results/SO_Normal_RRA_StaticOptimization_activation.sto\")\n", + "\n", + "# Get values.\n", + "time_SO_Normal_RRA = table_activation_SO_Normal_RRA.getIndependentColumn()\n", + "gastrocnemius_muscle_activation_normal_rra = table_activation_SO_Normal_RRA.getDependentColumn(\"gastroc_r\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DTDNDHKwEuoD" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Gastrocnemius Activation')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(time_SO_Normal_RRA, gastrocnemius_muscle_activation_normal_rra.to_numpy(), label='Normal')\n", + "axs.plot(time_SO_Strong_RRA, gastrocnemius_muscle_activation_strong_rra.to_numpy(), label='Strong')\n", + "axs.set_xlabel('Time (s)')\n", + "axs.set_ylabel('Activation')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wsqvkJZtySfS" + }, + "source": [ + "Plot and compare the lumbar actuator moment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OXnYt2D0yTLh" + }, + "outputs": [], + "source": [ + "# Get results from file.\n", + "table_forces_SO_Strong_RRA = osim.TimeSeriesTable(\n", + " \"SO_Strong_RRA_Results/SO_Strong_RRA_StaticOptimization_force.sto\")\n", + "\n", + "# Get values.\n", + "time_SO_Strong_RRA = table_forces_SO_Strong_RRA.getIndependentColumn()\n", + "lumbar_actuator_moment_strong_rra = table_forces_SO_Strong_RRA.getDependentColumn(\"lumbar_extension_reserve\")\n", + "\n", + "# Get results from file.\n", + "table_forces_SO_Normal_RRA = osim.TimeSeriesTable(\n", + " \"SO_Normal_RRA_Results/SO_Normal_RRA_StaticOptimization_force.sto\")\n", + "\n", + "# Get values.\n", + "time_SO_Normal_RRA = table_forces_SO_Normal_RRA.getIndependentColumn()\n", + "lumbar_actuator_moment_normal_rra = table_forces_SO_Normal_RRA.getDependentColumn(\"lumbar_extension_reserve\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "h4vIMTvxHTw7" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Lumbar Actuator Moment')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(time_SO_Normal_RRA, lumbar_actuator_moment_normal_rra.to_numpy(), label='Normal')\n", + "axs.plot(time_SO_Strong_RRA, lumbar_actuator_moment_strong_rra.to_numpy(), label='Strong')\n", + "axs.set_xlabel('Time')\n", + "axs.set_ylabel('Moment (Nm)')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sBicC1_Hu90Y" + }, + "source": [ + "Plot and compare the ankle reserve actuator moment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "r4SKFl0H3O8V" + }, + "outputs": [], + "source": [ + "# Get results from file.\n", + "table_forces_SO_Strong_RRA = osim.TimeSeriesTable(\n", + " \"SO_Strong_RRA_Results/SO_Strong_RRA_StaticOptimization_force.sto\")\n", + "\n", + "# Get values.\n", + "time_SO_Strong_RRA = table_forces_SO_Strong_RRA.getIndependentColumn()\n", + "ankle_r_actuator_moment_strong_rra = table_forces_SO_Strong_RRA.getDependentColumn(\"ankle_angle_r_reserve\")\n", + "\n", + "# Get results from file.\n", + "table_forces_SO_Normal_RRA = osim.TimeSeriesTable(\n", + " \"SO_Normal_RRA_Results/SO_Normal_RRA_StaticOptimization_force.sto\")\n", + "\n", + "# Get values.\n", + "time_SO_Normal_RRA = table_forces_SO_Normal_RRA.getIndependentColumn()\n", + "ankle_r_actuator_moment_normal_rra = table_forces_SO_Normal_RRA.getDependentColumn(\"ankle_angle_r_reserve\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "L5wO-niVHnQL" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Ankle Actuator Moment')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(time_SO_Normal_RRA, ankle_r_actuator_moment_normal_rra.to_numpy(), label='Normal')\n", + "axs.plot(time_SO_Strong_RRA, ankle_r_actuator_moment_strong_rra.to_numpy(), label='Strong')\n", + "axs.set_xlabel('Time')\n", + "axs.set_ylabel('Moment (Nm)')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kIg-uimyvGb6" + }, + "source": [ + "Finally, plot and compare the `Fx` residual force when using the strong actuators (optimal force at 100) and normal actuators (optimal force at 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TrSzwaOL3nJN" + }, + "outputs": [], + "source": [ + "# Get results from file.\n", + "table_forces_SO_Strong_RRA = osim.TimeSeriesTable(\n", + " \"SO_Strong_RRA_Results/SO_Strong_RRA_StaticOptimization_force.sto\")\n", + "\n", + "# Get values.\n", + "time_SO_Strong_RRA = table_forces_SO_Strong_RRA.getIndependentColumn()\n", + "fx_strong_rra = table_forces_SO_Strong_RRA.getDependentColumn(\"FX\")\n", + "\n", + "# Get results from file.\n", + "table_forces_SO_Normal_RRA = osim.TimeSeriesTable(\n", + " \"SO_Normal_RRA_Results/SO_Normal_RRA_StaticOptimization_force.sto\")\n", + "\n", + "# Get values.\n", + "time_SO_Normal_RRA = table_forces_SO_Normal_RRA.getIndependentColumn()\n", + "fx_normal_rra = table_forces_SO_Normal_RRA.getDependentColumn(\"FX\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "69CuU10KHxEy" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Fx Residual Force')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(time_SO_Normal_RRA, fx_normal_rra.to_numpy(), label='Normal')\n", + "axs.plot(time_SO_Strong_RRA, fx_strong_rra.to_numpy(), label='Strong')\n", + "axs.set_xlabel('Time')\n", + "axs.set_ylabel('Force (N)')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vDOFJWSu33fZ" + }, + "source": [ + "**Questions**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jTTT7T3Qn5ib" + }, + "source": [ + "* How does the gastrocnemius muscle force change between the analysis using strong actuators (optimal force at 100) and the normal (optimal force at 1) actuators? What about the moment generated by the ankle reserve actuators? If there are differences, explain why." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H5SWd_aqn7SP" + }, + "source": [ + "* How does the lumbar actuator reserve moment change between using the strong vs. normal optimal force? Why might this be the case? (Hint: observe whether or not there are muscles that control the lumbar extension degree of freedom.)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UuHlogCyn8t0" + }, + "source": [ + "* What is the peak `Fx` residual force? Why does the value stay the same between the strong and normal actuator case? (Hint: refer to the section earlier in the tutorial where we discuss why we need residual forces and moments. Are there other actuators in the model that could generate these forces?)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zw-iFZjs4N3E" + }, + "source": [ + "## 6.6. Study 4: Static Optimization with a passive element \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dFVokuxe4RG1" + }, + "source": [ + "In this Study, you will add an ankle spring to the model and observe the effect on gastrocnemius activation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Iqz8EkHQvZzq" + }, + "source": [ + "**Add a Spring to the Model**\n", + "\n", + "The first step is to add a coordinate spring to the model. The following cell adds the spring for you. A new model named `gait10dof18musc_spring.osim` will be generated. Explore the different properties of the spring (e.g., stiffness, limits, damping, etc.)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DuCHCy594v54" + }, + "outputs": [], + "source": [ + "# Get a handle to the current model and create a new copy \n", + "ankleSpringModel = osim.Model('gait10dof18musc_simbody.osim')\n", + "ankleSpringModel.setName(ankleSpringModel.getName()+'_ankle_spring')\n", + "\n", + "# Create the spring we'll add to the model (a CoordinateLimitForce in OpenSim)\n", + "ankleSpring = osim.CoordinateLimitForce()\n", + "\n", + "# Set the coordinate for the spring\n", + "ankleSpring.set_coordinate('ankle_angle_r')\n", + "ankleSpring.setName('AnkleLimitSpringDamper')\n", + "\n", + "# Add the spring to the model\n", + "ankleSpringModel.addForce(ankleSpring)\n", + "\n", + "# Set the spring's properties\n", + "ankleSpring.setUpperStiffness(30.0)\n", + "ankleSpring.setUpperLimit(5.0)\n", + "ankleSpring.setLowerStiffness(1.0)\n", + "ankleSpring.setLowerLimit(-90.0)\n", + "ankleSpring.setDamping(0.01)\n", + "ankleSpring.setTransition(2.0)\n", + "\n", + "# Save the model to file\n", + "fullPathName = ankleSpringModel.getInputFileName()\n", + "newName = fullPathName.replace('.osim', '_spring.osim')\n", + "ankleSpringModel.printToXML(newName)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sjZ6ynSh5sbg" + }, + "source": [ + "**Create Static Optimization Analysis Tool**\n", + "\n", + "Now that you have a new model with an ankle spring, you will re-run the Static Optimization analysis using the same setup from Study 3 (with normal reserve actuators). Rename the output folder to `\\SO_Normal_RRA_Spring_Results`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wL5Qr7NO57Jb" + }, + "outputs": [], + "source": [ + "# Create analyze tool for static optimization.\n", + "so_normal_RRA_spring_analyze_tool = osim.AnalyzeTool()\n", + "so_normal_RRA_spring_analyze_tool.setName(\"SO_Normal_RRA_Spring\")\n", + "\n", + "# Add actuators file.\n", + "forceSet_files = osim.ArrayStr()\n", + "forceSet_files.append(\"gait10dof18musc_Actuators_Normal.xml\")\n", + "so_normal_RRA_spring_analyze_tool.setForceSetFiles(forceSet_files)\n", + "\n", + "# Set model file, motion files and external load file names.\n", + "so_normal_RRA_spring_analyze_tool.setModelFilename(\"gait10dof18musc_simbody_spring.osim\")\n", + "so_normal_RRA_spring_analyze_tool.setCoordinatesFileName(\"subject_adjusted_Kinematics_q.sto\")\n", + "so_normal_RRA_spring_analyze_tool.setExternalLoadsFileName(\"subject01_walk_grf.xml\")\n", + "\n", + "# Add analysis.\n", + "so_normal_RRA_spring_analyze_tool.updAnalysisSet().cloneAndAppend(so_rra)\n", + "\n", + "# Configure analyze tool.\n", + "so_normal_RRA_spring_analyze_tool.setReplaceForceSet(False)\n", + "so_normal_RRA_spring_analyze_tool.setStartTime(start_time)\n", + "so_normal_RRA_spring_analyze_tool.setFinalTime(end_time)\n", + "\n", + "# Directory where results are stored.\n", + "so_normal_RRA_spring_analyze_tool.setResultsDir(\"SO_Normal_RRA_Spring_Results\")\n", + "\n", + "# Print configuration of analyze tool to a xml file.\n", + "so_normal_RRA_spring_analyze_tool.printToXML(\"SO_Normal_RRA_Spring_AnalyzeTool_setup.xml\")\n", + "\n", + "# Load configuration and run the analyses. \n", + "so_normal_RRA_spring_analyze_tool = osim.AnalyzeTool(\"SO_Normal_RRA_Spring_AnalyzeTool_setup.xml\", True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "80i0nMGmwHki" + }, + "source": [ + "**Perform Static Optimization**\n", + "\n", + "We can now perform Static Optimization on the model with the ankle spring." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "r_rlitXC57Jc" + }, + "outputs": [], + "source": [ + "# Run static optimization.\n", + "so_normal_RRA_spring_analyze_tool.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "elsHoKnQ6rmg" + }, + "source": [ + "**Plot Results**\n", + "\n", + "Plot the AnkleLimitSpringDamper moment from the file `SO_Normal_RRA_Spring_Results/SO_Normal_RRA_Spring_StaticOptimization_force.sto`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lroKJsWX6wYp" + }, + "outputs": [], + "source": [ + "# Get results from file.\n", + "table_forces_SO_Normal_RRA_Spring = osim.TimeSeriesTable(\n", + " \"SO_Normal_RRA_Spring_Results/SO_Normal_RRA_Spring_StaticOptimization_force.sto\")\n", + "\n", + "# Get values.\n", + "time_SO_Strong_RRA = table_forces_SO_Normal_RRA_Spring.getIndependentColumn()\n", + "ankle_limit_spring_damper = table_forces_SO_Normal_RRA_Spring.getDependentColumn(\"AnkleLimitSpringDamper\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "STLaD20tH93L" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Ankle Coordinate Spring')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(time_SO_Strong_RRA, ankle_limit_spring_damper.to_numpy(), label='Ankle Spring')\n", + "axs.set_xlabel('Time (s)')\n", + "axs.set_ylabel('Ankle Limit Spring Damper')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RqkdppgY7ygX" + }, + "source": [ + "Plot the Gastrocnemius activation from the analyzes with and without the Spring (the results should be in the folder `SO_Normal_RRA_Results` from Study 4 and the latest folder, `SO_Normal_RRA_Spring_Results`)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PGI07Ea572rf" + }, + "outputs": [], + "source": [ + "# Get results from file.\n", + "table_activation_SO_Normal_RRA_Spring = osim.TimeSeriesTable(\n", + " \"SO_Normal_RRA_Spring_Results/SO_Normal_RRA_Spring_StaticOptimization_activation.sto\")\n", + "\n", + "# Get values.\n", + "time_SO_Normal_RRA_Spring = table_activation_SO_Normal_RRA_Spring.getIndependentColumn()\n", + "gastrocnemius_muscle_activation_normal_rra_spring = table_activation_SO_Normal_RRA_Spring.getDependentColumn(\"gastroc_r\")\n", + "\n", + "# Get results from file.\n", + "table_activation_SO_Normal_RRA = osim.TimeSeriesTable(\n", + " \"SO_Normal_RRA_Results/SO_Normal_RRA_StaticOptimization_activation.sto\")\n", + "\n", + "# Get values.\n", + "time_SO_Normal_RRA = table_activation_SO_Normal_RRA.getIndependentColumn()\n", + "gastrocnemius_muscle_activation_normal_rra = table_activation_SO_Normal_RRA.getDependentColumn(\"gastroc_r\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eCXPMru9IKn5" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 1, figsize=(7, 5))\n", + "fig.suptitle('Gastrocnemius Activation')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs.plot(time_SO_Normal_RRA, gastrocnemius_muscle_activation_normal_rra_spring.to_numpy(), label='Spring')\n", + "axs.plot(time_SO_Strong_RRA, gastrocnemius_muscle_activation_normal_rra.to_numpy(), label='No Spring')\n", + "axs.set_xlabel('Time (s)')\n", + "axs.set_ylabel('Activation')\n", + "axs.grid()\n", + "axs.legend()\n", + "\n", + "# Set the spacing between subplots\n", + "plt.subplots_adjust(wspace=0.5, hspace=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bT4OJvLz8-0J" + }, + "source": [ + "**Questions**:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Tvb9ALd9oJAv" + }, + "source": [ + "* Why did the gastrocnemius activation decrease when the ankle spring was added?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rFr_vdRjoKkw" + }, + "source": [ + "* Optional: try increasing or decreasing the spring stiffness. How does this affect the gastrocnemius activation? " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rC_GQWCuoL8o" + }, + "source": [ + "* Optional: are any other muscles affected by adding the ankle spring?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i7rhPT2id6gg" + }, + "source": [ + "## 6.7. Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1XK9UgItWQMF" + }, + "source": [ + "In this tutorial you have learned how to perform a Static Optimization Analysis, to troubleshoot the different problems associated with it by filtering the motion, using RRA, and tuning the reserve and residual actuators. Also, you have learned to ann a passive element to your model and perform a Static Optimization analysis with it." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lLWgd8Noun6Y" + }, + "source": [ + "## 6.8. Useful Links\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m8M3dlT5xQcD" + }, + "source": [ + "> **OpenSim Website:** https://opensim.stanford.edu/\n", + ">\n", + "> **OpenSim Python Scripting:** https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53085346/Scripting+in+Python\n", + ">\n", + "> **OpenSim API Documentation:** https://simtk.org/api_docs/opensim/api_docs/\n", + "> \n", + "> **OpenSim Creator Website:** https://opensimcreator.com/\n", + "> \n", + "> **SimTK Website:** https://simtk.org/projects/opensim\n", + "> \n", + "> **Biomechanics of Movement's Course:** https://www.youtube.com/channel/UCDNGy0KKNLQ-ztcL5h2Z6zA" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rFR2Rf9tk_9A" + }, + "source": [ + "## 6.9 Acknowledgments\n", + "\n", + "Thanks to [OpenSimColab](https://simtk.org/projects/opencolab) project [[3]](https://doi.org/10.1080/10255842.2022.2104607) for creating the first OpenSim Conda package." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dxMjdukAnH2N" + }, + "source": [ + "## 6.10. References\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WX2A_iaYulV1" + }, + "source": [ + "> [1]. Delp, S. L., Anderson, F. C., Arnold, A. S., Loan, P., Habib, A., John, C. T., Guendelman, E., & Thelen, D. G. (2007). **OpenSim: open-source software to create and analyze dynamic simulations of movement.** *IEEE Transactions on Bio-Medical Engineering*, 54(11), 1940–1950. https://doi.org/10.1109/TBME.2007.901024\n", + "> \n", + "> [2] Hicks, J. L., Uchida, T. K., Seth, A., Rajagopal, A., & Delp, S. L. (2015). **Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of movement.** *Journal of Biomechanical Engineering*, 137(2), 020905. https://doi.org/10.1115/1.4029304\n", + ">\n", + "> [3] Mokhtarzadeh, H., Jiang, F., Zhao, S., & Malekipour, F. (2022). **OpenColab project: OpenSim in Google colaboratory to explore biomechanics on the web.** *Computer Methods in Biomechanics and Biomedical Engineering*, 1–9. https://doi.org/10.1080/10255842.2022.2104607" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "opensim", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Bindings/Python/tutorials/Tutorial 7 - Introduction to OpenSim Moco.ipynb b/Bindings/Python/tutorials/Tutorial 7 - Introduction to OpenSim Moco.ipynb new file mode 100644 index 0000000000..984c43bc9e --- /dev/null +++ b/Bindings/Python/tutorials/Tutorial 7 - Introduction to OpenSim Moco.ipynb @@ -0,0 +1,471 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YS-0eq5Y6BjU" + }, + "source": [ + "# 7. Introduction to OpenSim Moco" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZZClpIjbitD4" + }, + "source": [ + "\n", + "## 7.1. Objectives" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AKANMMa6iycq" + }, + "source": [ + "**Introduction**\n", + "\n", + "[OpenSim Moco](https://opensim-org.github.io/opensim-moco-site/) is a software package for creating musculoskeletal simulations using trajectory optimization [[1]](https://doi.org/10.1371/journal.pcbi.1008493). Users define problems by providing an OpenSim model, cost function terms, and additional problem constraints via an easy-to-use scripting interface. Moco rapidly solves trajectory optimization problems via the state-of-the-art direct collocation method.\n", + "\n", + "**Purpose**\n", + "\n", + "The purpose of this tutorial is to demonstrate how to use Moco to build, solve, and analyze a simple trajectory optimization problem using the [OpenSim conda package](https://anaconda.org/opensim-org/opensim) and [Jupyter notebooks](https://jupyter.org/). In this tutorial you will:\n", + "\n", + "* Set up a new conda environment and install OpenSim with Moco.\n", + "* Learn the basics of Moco's `MocoStudy` interface for defining optimal control problems.\n", + "* Solve Moco's simplest example, the \"sliding mass\" problem.\n", + "* Plot the results using `matplotlib`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Sliding Mass Problem**\n", + "\n", + "You will use Moco to find the optimal trajectory of a point mass starting at rest, translating one meter, and ending at rest, in minimum time.\n", + "\n", + "\n", + "![sliding_mass](https://drive.google.com/uc?export=download&id=17YTG2WqP6Hzg83OwGyOjqXmyqIWyl34s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Local Installation**\n", + "\n", + "You may also run this notebook using a locally installed OpenSim `conda` package. Visit [the OpenSim python scripting Confluence page](https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53085346/Scripting+in+Python) for more information on how to install OpenSim in a local Python environment. You will also need to download the resources from Google Drive to the same directory as this notebook. Then, you may skip to Section 7.3 below." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kPnGlZIT8oiM" + }, + "source": [ + "## 7.2. Install `conda` and OpenSim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3kxYrs4Hi6Nl" + }, + "source": [ + "In the following cell, you will use [condacolab](https://github.com/conda-incubator/condacolab) to install [Miniconda](https://docs.conda.io/en/latest/miniconda.html). If you want to install a different conda distribution (e.g., Anaconda, Mambaforge) check [condacolab's documentation](https://github.com/conda-incubator/condacolab#usage)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5-sX9L3f6Asq", + "outputId": "07c714c8-7744-4bf4-fa0f-f1c66d2fcec9" + }, + "outputs": [], + "source": [ + "!pip install -q condacolab\n", + "import condacolab\n", + "condacolab.install()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LdmpfqC8Dt0x" + }, + "source": [ + "Don't worry if after executing the previous cell you get an error saying that your session has failed. This happens because `condacolab` needs to restart the session for the changes to take effect. Therefore, you have to execute the previous cell before executing any other cell.\n", + "\n", + "Now, execute the following cell to install the OpenSim `conda` package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "H9WE2h3fD61J", + "outputId": "2795df96-b021-40eb-ba47-39a105cc2ac6" + }, + "outputs": [], + "source": [ + "!conda install opensim-org::opensim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.3. Import the OpenSim module" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BV3ofVUoEkEi" + }, + "source": [ + "The following cell imports the OpenSim module and, if successful, shows the current version and build date." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import opensim as osim\n", + "osim.GetVersionAndDate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.4. Create the sliding mass model\n", + "\n", + "First, let's create an OpenSim `Model` representing a \"sliding mass\": a point mass model with a 1-DOF `SliderJoint` actuated by a `CoordinateActuator`. Moco will use this model to enforce the dynamics defect constraints in the direct collocation problem we will solve below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uCT1o9zzF-Nn" + }, + "outputs": [], + "source": [ + "model = osim.Model()\n", + "model.setName('sliding_mass')\n", + "model.set_gravity(osim.Vec3(0, 0, 0))\n", + "body = osim.Body('body', 2.0, osim.Vec3(0), osim.Inertia(0))\n", + "model.addComponent(body)\n", + "\n", + "# Allows translation along x.\n", + "joint = osim.SliderJoint('slider', model.getGround(), body)\n", + "coord = joint.updCoordinate()\n", + "coord.setName('position')\n", + "model.addComponent(joint)\n", + "\n", + "actu = osim.CoordinateActuator()\n", + "actu.setCoordinate(coord)\n", + "actu.setName('actuator')\n", + "actu.setOptimalForce(1)\n", + "model.addComponent(actu)\n", + "\n", + "body.attachGeometry(osim.Sphere(0.05))\n", + "\n", + "model.finalizeConnections()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.5. Create a `MocoStudy`\n", + "\n", + "`MocoStudy` is the most flexible interface for defining optimal control problems in Moco. It encapsulates both a `MocoProblem`, the optimal control problem definition, and a `MocoSolver`, the module which converts the optimal control problem to a nonlinear program using direct collocation.\n", + "\n", + "Run the following code cell to create a new `MocoStudy`, access the underlying `MocoProblem`, and assign the problem the `Model` we created above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create MocoStudy.\n", + "# ================\n", + "study = osim.MocoStudy()\n", + "study.setName('sliding_mass')\n", + "\n", + "# Define the optimal control problem.\n", + "# ===================================\n", + "problem = study.updProblem()\n", + "\n", + "# Model (dynamics).\n", + "# -----------------\n", + "problem.setModel(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.6. Define the state bounds and objective function\n", + "\n", + "The sliding mass problem is a boundary value problem (BVP), so we need to set some bounds for the state variables, e.g., the mass position and speed. Run the following cell to set the initial position to 0, the final position to 1, and the initial and final speeds to zero. \n", + "\n", + "This cell also configures a minimum time objective function using `MocoFinalTimeGoal`. Since we are optimizing time, the final time bounds are set to the interval `[0, 5]` seconds." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Bounds.\n", + "# -------\n", + "# Initial time must be 0, final time can be within [0, 5].\n", + "problem.setTimeBounds(osim.MocoInitialBounds(0.), osim.MocoFinalBounds(0., 5.))\n", + "\n", + "# Position must be within [-5, 5] throughout the motion.\n", + "# Initial position must be 0, final position must be 1.\n", + "problem.setStateInfo('/slider/position/value', osim.MocoBounds(-5, 5),\n", + " osim.MocoInitialBounds(0), osim.MocoFinalBounds(1))\n", + "# Speed must be within [-50, 50] throughout the motion.\n", + "# Initial and final speed must be 0. Use compact syntax.\n", + "problem.setStateInfo('/slider/position/speed', [-50, 50], [0], [0])\n", + "\n", + "# Applied force must be between -50 and 50.\n", + "problem.setControlInfo('/actuator', osim.MocoBounds(-50, 50))\n", + "\n", + "# Cost.\n", + "# -----\n", + "problem.addGoal(osim.MocoFinalTimeGoal())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.7. Solve the direct collocation problem\n", + "\n", + "Next, we will use `MocoCasADiSolver` to transcribe our optimal control problem to a nonlinear program (NLP) using direct collocation. This `MocoSolver` uses CasADi [[2]](https://doi.org/10.1007/s12532-018-0139-4), an open-source software framework for numerical optimization. Internally, Moco uses CasADi to convert your `MocoProblem` to a direct collocation problem written using CasADi's symbolic and numerical types.\n", + "\n", + "Run the following cell to intialize the `MocoCasADiSolver`, solve the the optimal control problem, and write the solution to a file. Note that if you want to re-run this cell, you will need to reset the Jupyter notebook to clear workspace variables since `study.initCasADiSolver()` cannot be called after a `MocoSolver` has already been initialized." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Configure the solver.\n", + "# =====================\n", + "solver = study.initCasADiSolver()\n", + "solver.set_num_mesh_intervals(100)\n", + "\n", + "# Now that we've finished setting up the study, print it to a file.\n", + "study.printToXML('sliding_mass.omoco')\n", + "\n", + "# Solve the problem.\n", + "# ==================\n", + "solution = study.solve()\n", + "\n", + "solution.write('sliding_mass_solution.sto')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bMYcGGuaetwS" + }, + "source": [ + "You should now have `sliding_mass.omoco` and `sliding_mass_solution.sto` in your files." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7.8. Plot the solution\n", + "\n", + "Finally, let's plot the solution. After solving the problem above, `MocoStudy` returned a `MocoSolution` containing the state and control trajectories which we can access directly in Python using `getStateMat()` and `getControlMat()`, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "time = solution.getTimeMat()\n", + "position = solution.getStateMat('/slider/position/value')\n", + "speed = solution.getStateMat('/slider/position/speed')\n", + "actuator = solution.getControlMat('/actuator')\n", + "\n", + "# Create six subplots, with 2 rows and 3 columns.\n", + "fig, axs = plt.subplots(1, 3, figsize=(12, 5))\n", + "fig.suptitle('Sliding Mass Solution')\n", + "\n", + "# Plot the knee angles on the first subplot.\n", + "axs[0].plot(time, position, label='position', lw=4)\n", + "axs[0].set_ylabel('position (m)')\n", + "axs[0].set_xlabel('time (s)')\n", + "\n", + "axs[1].plot(time, speed, label='speed', lw=4)\n", + "axs[1].set_ylabel('speed (m/s)')\n", + "axs[1].set_xlabel('time (s)')\n", + "\n", + "axs[2].plot(time, actuator, label='actuator', lw=4)\n", + "axs[2].set_ylabel('actuator force (N)')\n", + "axs[2].set_xlabel('time (s)')\n", + "\n", + "for ax in axs:\n", + " ax.set_xlim(0, time[-1])\n", + " ax.grid()\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Questions**\n", + "\n", + "* Describe the state and control trajectories returned after solving the sliding mass problem. Given the optimal control problem defined above, does this solution make sense to you? Explain. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J5mhS9zNX9ff" + }, + "source": [ + "## 7.9. Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_M04jPJLuiUz" + }, + "source": [ + "In this tutorial you used [condacolab](https://github.com/conda-incubator/condacolab) to install [Miniconda](https://docs.conda.io/en/latest/miniconda.html) in [Google Colab](https://colab.research.google.com/?hl=en). Then, you installed Conda [OpenSim](https://opensim.stanford.edu/) with Moco into the Miniconda environment. You used the OpenSim API to build a \"sliding mass\" model, and then used Moco to define an optimal control problem using the `MocoStudy` interface. Finally, you plotted the results using `matplotlib`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lLWgd8Noun6Y" + }, + "source": [ + "## 7.10. Useful Links" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m8M3dlT5xQcD" + }, + "source": [ + "> **OpenSim Website:** https://opensim.stanford.edu/\n", + ">\n", + "> **OpenSim Moco Website:** https://opensim-org.github.io/opensim-moco-site/\n", + ">\n", + "> **OpenSim Python Scripting:** https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53085346/Scripting+in+Python\n", + ">\n", + "> **OpenSim API Documentation:** https://simtk.org/api_docs/opensim/api_docs/\n", + ">\n", + "> **OpenSim Creator Website:** https://opensimcreator.com/\n", + ">\n", + "> **SimTK Website:** https://simtk.org/projects/opensim\n", + ">\n", + "> **Biomechanics of Movement Course Videos:** https://www.youtube.com/channel/UCDNGy0KKNLQ-ztcL5h2Z6zA" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rFR2Rf9tk_9A" + }, + "source": [ + "## 7.11. Acknowledgments\n", + "\n", + "Thanks to [OpenSimColab](https://simtk.org/projects/opencolab) project [[3]](https://doi.org/10.1080/10255842.2022.2104607) for creating the first OpenSim Conda package." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dxMjdukAnH2N" + }, + "source": [ + "## 7.12. References" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WX2A_iaYulV1" + }, + "source": [ + "> [1] Dembia CL, Bianco NA, Falisse A, Hicks JL, Delp SL (2020) **OpenSim Moco: Musculoskeletal optimal control.** *PLOS Computational Biology* 16(12): e1008493. https://doi.org/10.1371/journal.pcbi.1008493\n", + ">\n", + "> [2] Andersson JAE, Gillis J, Horn G, Rawlings JB, Diehl M (2019) **CasADi – A software framework for nonlinear optimization\n", + "and optimal control.** *Mathematical Programming Computation* 11(1): 1-36. https://doi.org/10.1007/s12532-018-0139-4\n", + ">\n", + "> [3] Mokhtarzadeh, H., Jiang, F., Zhao, S., & Malekipour, F. (2022). **OpenColab project: OpenSim in Google colaboratory to explore biomechanics on the web.** *Computer Methods in Biomechanics and Biomedical Engineering*, 1–9. https://doi.org/10.1080/10255842.2022.2104607" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "opensim", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Bindings/Python/tutorials/Tutorial 8 - Creating Muscle-driven Simulations with OpenSim Moco.ipynb b/Bindings/Python/tutorials/Tutorial 8 - Creating Muscle-driven Simulations with OpenSim Moco.ipynb new file mode 100644 index 0000000000..ade840a55a --- /dev/null +++ b/Bindings/Python/tutorials/Tutorial 8 - Creating Muscle-driven Simulations with OpenSim Moco.ipynb @@ -0,0 +1,1003 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github" + }, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YS-0eq5Y6BjU" + }, + "source": [ + "# 8. Creating muscle-driven simulations with OpenSim Moco" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZZClpIjbitD4" + }, + "source": [ + "\n", + "## 8.1. Objectives" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AKANMMa6iycq" + }, + "source": [ + "**Introduction**\n", + "\n", + "[OpenSim Moco](https://opensim-org.github.io/opensim-moco-site/) is a software package for creating musculoskeletal simulations using trajectory optimization [[1]](https://doi.org/10.1371/journal.pcbi.1008493). Users define problems by providing an OpenSim model, cost function terms, and additional problem constraints via an easy-to-use scripting interface. Moco rapidly solves trajectory optimization problems via the state-of-the-art direct collocation method.\n", + "\n", + "**Purpose**\n", + "\n", + "The purpose of this tutorial is to showcase Moco's advanced simulation capabilities using the [OpenSim conda package](https://anaconda.org/opensim-org/opensim) and [Jupyter notebooks](https://jupyter.org/). In this tutorial you will:\n", + "\n", + "* Set up a new conda environment and install OpenSim with Moco.\n", + "* Learn how to replace muscle geometry paths with smooth function-based paths better suited for optimization.\n", + "* Pose and solve a muscle-driven optimization problem for simulating a squat-to-stand motion.\n", + "* Design a passive assistive device at the knee using parameter optimization.\n", + "* Investigate how neural control limitations affect the squat-to-stand motion using muscle synergies.\n", + "\n", + "**Resources**\n", + "\n", + "All of the files necessary to complete this tutorial are available in the following Google Drive folder: https://drive.google.com/drive/folders/1eGe2UxcsYmT3OXQN3AdpgnrJy803sdci?usp=sharing\n", + "\n", + "While you complete the tutorial, you will find cells that automatically download the required files.\n", + "\n", + "To obtain a link to one of the files in Google Drive, right-click over it and click on **Get Link**." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kPnGlZIT8oiM" + }, + "source": [ + "## 8.2. Set up conda environment and download resources" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3kxYrs4Hi6Nl" + }, + "source": [ + "In the following cell, you will use [condacolab](https://github.com/conda-incubator/condacolab) to install [Miniconda](https://docs.conda.io/en/latest/miniconda.html). If you want to install a different conda distribution (e.g., Anaconda, Mambaforge) check [condacolab's documentation](https://github.com/conda-incubator/condacolab#usage)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5-sX9L3f6Asq", + "outputId": "07c714c8-7744-4bf4-fa0f-f1c66d2fcec9" + }, + "outputs": [], + "source": [ + "!pip install -q condacolab\n", + "import condacolab\n", + "condacolab.install()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LdmpfqC8Dt0x" + }, + "source": [ + "Don't worry if after executing the previous cell you get an error saying that your session has failed. The reason for this is that condacolab needs to restart the session for the changes to take effect. Because of this, you have to execute the previous cell first, before executing any other cell.\n", + "\n", + "Now, execute the following cell to install the OpenSim conda package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "H9WE2h3fD61J", + "outputId": "2795df96-b021-40eb-ba47-39a105cc2ac6" + }, + "outputs": [], + "source": [ + "!conda install opensim-org::opensim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, execute the following cell to create a button that will download the \"squat-to-stand\" OpenSim [`Model`](https://simtk.org/api_docs/opensim/api_docs451/classOpenSim_1_1Model.html). This is a single leg with 3 DOF and 9 muscles, and the foot is welded to the ground. The model file and accompanying geometry files will be extracted to the working directory.\n", + "\n", + "![model](https://drive.google.com/uc?export=view&id=1YE2lS4_jCnsFsev0GSbR_OGiXpXC3iMS)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# @title Download the \"squat-to-stand\" model and geometry files { display-mode: \"form\" }\n", + "# @markdown After executing this cell, a download button will appear. Click the button to download and extract the zip file containing the \"squat-to-stand\" model and geometry files.\n", + "\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + "import requests\n", + "import zipfile\n", + "import io\n", + "\n", + "# Define the function to download and extract the zip file\n", + "def download_and_extract_zip(url):\n", + " try:\n", + " # Get the content of the URL\n", + " response = requests.get(url)\n", + " response.raise_for_status()\n", + "\n", + " # Create a ZipFile object from the downloaded content\n", + " with zipfile.ZipFile(io.BytesIO(response.content)) as zip_ref:\n", + " # Extract all the contents into the working directory\n", + " zip_ref.extractall()\n", + " print(\"Extraction complete!\")\n", + " except requests.exceptions.RequestException as e:\n", + " print(f\"Failed to download the file: {e}\")\n", + " except zipfile.BadZipFile:\n", + " print(\"The file is not a valid zip file.\")\n", + " except Exception as e:\n", + " print(f\"An error occurred: {e}\")\n", + "\n", + "# Create a button to trigger the download and extraction\n", + "download_button = widgets.Button(\n", + " description='Download Model',\n", + " disabled=False,\n", + " button_style='',\n", + " tooltip='Click to download the squat-to-stand model and geometry files',\n", + " icon='download'\n", + ")\n", + "\n", + "# Define the button click event handler\n", + "def on_button_click(b):\n", + " url = 'https://drive.google.com/uc?export=download&id=1xnrBNTqIHQVIIxQNQEbHUmVLLRduV5jd'\n", + " if url:\n", + " download_and_extract_zip(url)\n", + " else:\n", + " print(\"Please enter a valid URL.\")\n", + "\n", + "# Attach the event handler to the button\n", + "download_button.on_click(on_button_click)\n", + "\n", + "# Display the widgets\n", + "display(download_button)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.3. Fit function-based paths for the squat-to-stand model \n", + "\n", + "Before we create a squat-to-stand simulation, we will replace the muscle geometry in the model with smooth polynomial functions which, being smooth and continuous everywhere, lead to better convergence in gradient-based optimization. The original model represents muscle geometry using [`GeometryPath`](https://simtk.org/api_docs/opensim/api_docs451/classOpenSim_1_1GeometryPath.html) elements. We will replace these with [`FunctionBasedPath`](https://simtk.org/api_docs/opensim/api_docs451/classOpenSim_1_1FunctionBasedPath.html) elements, which will compute muscle length, speed, and moment arms using OpenSim's [`MultivariatePolynomialFunction`](https://simtk.org/api_docs/opensim/api_docs451/classOpenSim_1_1MultivariatePolynomialFunction.html) class. The polynomials need to be multivariate since muscle paths can depend on multiple coordinates in the model (e.g., the gastrocnemius path depends on both the knee and ankle coordinates)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, run the following cell to import OpenSim and set the logging file. If successful, it will print version 4.5.1 with build date in July 2024." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import opensim as osim\n", + "osim.Logger.removeFileSink()\n", + "osim.Logger.addFileSink('opensim.log')\n", + "osim.GetVersionAndDate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, run the following cell to use forward integration to create a random trajectory that we can use as a reference when fitting polynomial functions to represent muscle lengths. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the model and initialize the multibody system\n", + "model = osim.Model('squatToStand_3dof9musc.osim')\n", + "state = model.initSystem()\n", + "\n", + "# Set an initial pose.\n", + "coordSet = model.updCoordinateSet()\n", + "coordSet.get('hip_flexion_r').setValue(state, -40 * (np.pi / 180))\n", + "coordSet.get('knee_angle_r').setValue(state, -45 * (np.pi / 180))\n", + "coordSet.get('ankle_angle_r').setValue(state, -25 * (np.pi / 180))\n", + "\n", + "# Run a 0.4 second forward simulation.\n", + "manager = osim.Manager(model, state)\n", + "manager.integrate(0.4)\n", + "\n", + "# Save the states to a file.\n", + "table = manager.getStatesTable()\n", + "sto = osim.STOFileAdapter()\n", + "if not os.path.exists('path_fitting'):\n", + " os.mkdir('path_fitting')\n", + "sto.write(table, os.path.join('path_fitting', 'reference_states.sto'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now use [`PolynomialPathFitter`](https://simtk.org/api_docs/opensim/api_docs451/classOpenSim_1_1PolynomialPathFitter.html), a utility class for automating the path fitting process. First, it will randomly sample coordinate values around the reference trajectory. Then, muscle lengths and moment arms will be computed from the sampled coordinate values. Coefficients for multivariate polynomial functions representing the muscle lengths are determined using a least-squares fit. Both the muscle lengths and the moment arms (via the polynomial derivatives) are included in the fitting process. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Construct a PolynomialPathFitter object.\n", + "fitter = osim.PolynomialPathFitter()\n", + "\n", + "# Set the model.\n", + "fitter.setModel(osim.ModelProcessor('squatToStand_3dof9musc.osim'))\n", + "\n", + "# Set the reference states.\n", + "table = osim.TimeSeriesTable(os.path.join('path_fitting', 'reference_states.sto'))\n", + "fitter.setCoordinateValues(osim.TableProcessor(table))\n", + "\n", + "# Configure settings.\n", + "fitter.setMaximumPolynomialOrder(6)\n", + "fitter.setNumSamplesPerFrame(10)\n", + "fitter.setGlobalCoordinateSamplingBounds(osim.Vec2(-30, 30))\n", + "fitter.setUseStepwiseRegression(True)\n", + "fitter.setPathLengthTolerance(1e-4)\n", + "fitter.setMomentArmTolerance(1e-4)\n", + "fitter.setOutputDirectory('path_fitting')\n", + "\n", + "# Run the path fitter.\n", + "fitter.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we'll plot the results to compare the muscle lengths computed from the fitted polynomial functions to the original muscle lengths." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# @title Plot the path fitting results { display-mode: \"form\" }\n", + "# @markdown Execute this cell to plot the fitted path lengths together with the original path lengths.\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "# Load the original and fitted path lengths and the path length based on the\n", + "# randomly sampled coordinates values.\n", + "lengths = osim.TimeSeriesTable(\n", + " os.path.join('path_fitting', 'squatToStand_path_lengths.sto'))\n", + "lengthsFitted = osim.TimeSeriesTable(\n", + " os.path.join('path_fitting', 'squatToStand_path_lengths_fitted.sto'))\n", + "lengthsSampled = osim.TimeSeriesTable(\n", + " os.path.join('path_fitting', 'squatToStand_path_lengths_sampled.sto'))\n", + "lengthsSampledFitted = osim.TimeSeriesTable(\n", + " os.path.join('path_fitting', 'squatToStand_path_lengths_sampled_fitted.sto'))\n", + "\n", + "# Plot the path lengths. Blue is the original, orange is the fitted.\n", + "fig, axs = plt.subplots(3, 3, figsize=(8, 8))\n", + "for ilabel, label in enumerate(lengths.getColumnLabels()):\n", + " irow = ilabel // 3\n", + " icol = ilabel % 3\n", + " axs[irow, icol].scatter(\n", + " lengthsSampled.getIndependentColumn(),\n", + " lengthsSampled.getDependentColumn(label).to_numpy(),\n", + " alpha=0.4, color='blue', s=0.3)\n", + " axs[irow, icol].scatter(\n", + " lengthsSampledFitted.getIndependentColumn(),\n", + " lengthsSampledFitted.getDependentColumn(label).to_numpy(),\n", + " alpha=0.4, color='orange', s=0.3)\n", + " axs[irow, icol].plot(\n", + " lengths.getIndependentColumn(),\n", + " lengths.getDependentColumn(label).to_numpy(),\n", + " label='original', lw=4)\n", + " axs[irow, icol].plot(\n", + " lengthsFitted.getIndependentColumn(),\n", + " lengthsFitted.getDependentColumn(label).to_numpy(),\n", + " label='fitted', lw=3.5)\n", + " title = label.replace('/forceset/', '').replace('_length', '')\n", + " axs[irow, icol].set_title(title)\n", + " if not irow and not icol:\n", + " axs[irow, icol].legend()\n", + " if not irow == 2:\n", + " axs[irow, icol].set_xticklabels([])\n", + " else:\n", + " axs[irow, icol].set_xlabel('time (s)')\n", + " if not icol:\n", + " axs[irow, icol].set_ylabel('muscle length (m)')\n", + "\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a good fit for our function-based muscle paths, we can move on to creating a squat-to-stand simulation with Moco." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.4. Muscle-driven simulation of a squat-to-stand motion\n", + "\n", + "In this section, we will use [`OpenSim Moco`](https://simtk.org/api_docs/opensim/api_docs451/mocomainpage.html) to create a predictive simulation of a motion starting from a squat and ending standing up. This is a **boundary-value problem**, since we need to impose hard constraints at the boundaries of the trajectory (i.e., the initial and final time point). The following image describes the coordinate boundary conditions for the squat and stand positions, and the coordinate value and speed bounds across the trajectory.\n", + "\n", + "![squat_to_stand](https://drive.google.com/uc?export=download&id=1ObBouDZnM2zrgnk6rsaCigg3qUr3hiLk)\n", + "\n", + "[`MocoStudy`](https://simtk.org/api_docs/opensim/api_docs451/mocostudy.html) is the most general interface for constructing and solving trajectory optimization problems in Moco. First, we will use [`MocoProblem`](https://simtk.org/api_docs/opensim/api_docs451/classOpenSim_1_1MocoProblem.html) to convert the mathematical description of the boundary-value problem above into a form that Moco understands. Next, we will configure a `MocoSolver`, in this case [`MocoCasADiSolver`](https://simtk.org/api_docs/opensim/api_docs451/classOpenSim_1_1MocoCasADiSolver.html), to specify [the problem transcription](https://simtk.org/api_docs/opensim/api_docs451/mocotheoryguide.html#dircol) and settings for the optimization solver. Finally, we can use the `MocoStudy` to solve the problem and return a [`MocoSolution`](https://simtk.org/api_docs/opensim/api_docs451/classOpenSim_1_1MocoSolution.html), which we can use to analyze and visualize the result. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Part 1: Create a new MocoStudy.\n", + "study = osim.MocoStudy()\n", + "study.setName('squat_to_stand_prediction')\n", + "\n", + "# Part 2: Initialize the problem.\n", + "problem = study.updProblem()\n", + "\n", + "# Part 3: Construct a model using the function-based paths. To simplify\n", + "# the problem, we will ignore tendon compliance, activation dynamics,\n", + "# and passive fiber forces.\n", + "modelProcessor = osim.ModelProcessor('squatToStand_3dof9musc.osim')\n", + "modelProcessor.append(osim.ModOpScaleMaxIsometricForce(2.0))\n", + "modelProcessor.append(osim.ModOpReplaceMusclesWithDeGrooteFregly2016())\n", + "modelProcessor.append(osim.ModOpIgnorePassiveFiberForcesDGF())\n", + "modelProcessor.append(osim.ModOpIgnoreTendonCompliance())\n", + "modelProcessor.append(osim.ModOpIgnoreActivationDynamics())\n", + "modelProcessor.append(osim.ModOpScaleActiveFiberForceCurveWidthDGF(1.5))\n", + "modelProcessor.append(osim.ModOpReplacePathsWithFunctionBasedPaths(\n", + " os.path.join('path_fitting', 'squatToStand_FunctionBasedPathSet.xml')))\n", + "model = modelProcessor.process()\n", + "problem.setModel(model)\n", + "\n", + "# Part 4: Set bounds on the problem.\n", + "#\n", + "# problem.setTimeBounds(initial_bounds, final_bounds)\n", + "# problem.setStateInfo(path, trajectory_bounds, inital_bounds, final_bounds)\n", + "#\n", + "# All *_bounds arguments can be set to a range, [lower upper], or to a\n", + "# single value (equal lower and upper bounds). Empty brackets, [], indicate\n", + "# using default bounds (if they exist). You may set multiple state infos at\n", + "# once using setStateInfoPattern():\n", + "#\n", + "# problem.setStateInfoPattern(pattern, trajectory_bounds, inital_bounds, ...\n", + "# final_bounds)\n", + "#\n", + "# This function supports regular expressions in the 'pattern' argument;\n", + "# use '.*' to match any substring of the state/control path\n", + "# For example, the following will set all coordinate value state infos:\n", + "#\n", + "# problem.setStateInfoPattern('/path/to/states/.*/value', ...)\n", + "\n", + "# Time bounds\n", + "problem.setTimeBounds(0, 1)\n", + "\n", + "# Position bounds: the model should start in a squat and finish\n", + "# standing up.\n", + "problem.setStateInfo('/jointset/hip_r/hip_flexion_r/value',\n", + " [-2, 0.5], -2, 0)\n", + "problem.setStateInfo('/jointset/knee_r/knee_angle_r/value',\n", + " [-2, 0], -2, 0)\n", + "problem.setStateInfo('/jointset/ankle_r/ankle_angle_r/value',\n", + " [-0.75, 0.75], -0.5, 0)\n", + "\n", + "# Velocity bounds: all model coordinates should start and end at rest.\n", + "problem.setStateInfoPattern('/jointset/.*/speed', [], 0, 0)\n", + "\n", + "# Part 5: Add a MocoControlCost to the problem.\n", + "problem.addGoal(osim.MocoControlGoal('control_effort'))\n", + "\n", + "# Part 6: Configure the solver.\n", + "solver = study.initCasADiSolver()\n", + "solver.set_num_mesh_intervals(25)\n", + "solver.set_optim_finite_difference_scheme('forward')\n", + "solver.set_optim_convergence_tolerance(1e-4)\n", + "solver.set_optim_constraint_tolerance(1e-4)\n", + "\n", + "# Part 7: Print the study to a file.\n", + "if not os.path.exists('squat_to_stand'):\n", + " os.mkdir('squat_to_stand')\n", + "\n", + "xml_path = os.path.join('squat_to_stand', 'squat_to_stand.xml')\n", + "if not os.path.exists(xml_path):\n", + " study.printToXML(xml_path)\n", + "\n", + "# Part 8: Solve! Write the solution to file.\n", + "predictSolution = study.solve()\n", + "solutionFile = os.path.join('squat_to_stand', 'squat_to_stand_prediction_solution.sto')\n", + "predictSolution.write(solutionFile)\n", + "# study.visualize(predictSolution)\n", + "\n", + "# Part 9: Create a PDF report of the solution.\n", + "output = os.path.join('squat_to_stand', 'squat_to_stand_prediction_report.pdf')\n", + "report = osim.report.Report(model,\n", + " solutionFile,\n", + " output=output,\n", + " bilateral=True)\n", + "report.generate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the following exercises to modify the example above and learn more about Moco's capabilities.\n", + "\n", + "**Exercise 1. Modify the problem to start from a standing position and end in a squat.**\n", + "\n", + "**Exercise 2. Change the cost function by adding a new [`MocoGoal`](https://simtk.org/api_docs/opensim/api_docs451/group__mocogoal.html) to the problem.**\n", + "\n", + "**Exercise 3. Advanced: create a new problem using [`MocoStateTrackingGoal`](https://simtk.org/api_docs/opensim/api_docs451/classOpenSim_1_1MocoStateTrackingGoal.html) to track a squat motion data recorded from OpenCap in Demo 1. See [`exampleMocoTrack`](https://simtk.org/api_docs/opensim/api_docs451/exampleMocoTrack_8py-example.html) for an example on how to use this `MocoGoal`.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.5. Designing a passive knee assistive device\n", + "\n", + "Next, we'll modify the squat-to-stand optimization problem to design a passive assistive device at the knee. We will model the \n", + "device using an OpenSim [`SpringGeneralizedForce`](https://simtk.org/api_docs/opensim/api_docs451/classOpenSim_1_1SpringGeneralizedForce.html) which applies a knee torque based on linear spring that is a function of the knee joint angle. The spring torque is parameterized by stiffness and rest length parameters (viscosity is set to zero). Note the rest length has angular units (radians), but we use \"rest length\" instead of \"rest angle\" to stay consistent with the OpenSim API documentation.\n", + "\n", + "![device_optimization](https://drive.google.com/uc?export=download&id=1LBG1lXRpfIjNGzTNt6xnt4rP-SO04orT)\n", + "\n", + "Execute the cell below to load an interactive menu for designing the assistive device. The menu provides two design models: \"Manual\" and \"Optimization\". In \"Manual\" mode, you will select a stiffness between [0, 400] N-m/rad and a rest length between [-1, 1] rad. In \"Optimization\" mode, the problem uses parameter optimization (via [`MocoParameter`](https://simtk.org/api_docs/opensim/api_docs451/classOpenSim_1_1MocoParameter.html)) to determine stiffness and rest length parameter values.\n", + "\n", + "While running the device optimizations, consider the following questions.\n", + "\n", + "**Question 1. How much can you reduce muscle effort via manual parameter design?**\n", + "\n", + "**Question 2. Do the optimized parameters make sense? Explain.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# @title Design a passive knee assistive device { display-mode: \"form\" }\n", + "# @markdown Execute the cell to load the interactive device design menu.\n", + "\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + "\n", + "def run_device_optimization_squat_to_stand(optimize, stiffness=50, rest_length=0):\n", + "\n", + " # Part 1: Load the base squat-to-stand MocoStudy.\n", + " study = osim.MocoStudy(os.path.join('squat_to_stand', 'squat_to_stand.xml'))\n", + " problem = study.updProblem()\n", + " solver = osim.MocoCasADiSolver.safeDownCast(study.updSolver())\n", + "\n", + " # Part 2: Recreate the model, now adding a passive assistive device at the knee\n", + " modelProcessor = osim.ModelProcessor('squatToStand_3dof9musc.osim')\n", + " modelProcessor.append(osim.ModOpScaleMaxIsometricForce(2.0))\n", + " modelProcessor.append(osim.ModOpReplaceMusclesWithDeGrooteFregly2016())\n", + " modelProcessor.append(osim.ModOpIgnorePassiveFiberForcesDGF())\n", + " modelProcessor.append(osim.ModOpIgnoreTendonCompliance())\n", + " modelProcessor.append(osim.ModOpScaleActiveFiberForceCurveWidthDGF(1.5))\n", + " modelProcessor.append(osim.ModOpReplacePathsWithFunctionBasedPaths(\n", + " os.path.join('path_fitting', 'squatToStand_FunctionBasedPathSet.xml')))\n", + " model = modelProcessor.process()\n", + "\n", + " # The device is a SpringGeneralizedForce acting on the knee angle.\n", + " device = osim.SpringGeneralizedForce('knee_angle_r')\n", + " device.setName('passive_knee_device')\n", + " device.setStiffness(stiffness)\n", + " device.setRestLength(rest_length)\n", + " device.setViscosity(0)\n", + " model.addForce(device)\n", + "\n", + " # Part 3: Update the problem with the new model.\n", + " problem.setModel(model)\n", + "\n", + " # Part 4: Add MocoParameters to the problem to optimize device stiffness and rest\n", + " # length.\n", + " if optimize:\n", + " stiffness = osim.MocoParameter('stiffness',\n", + " '/forceset/passive_knee_device', 'stiffness', osim.MocoBounds(0, 400))\n", + " problem.addParameter(stiffness)\n", + "\n", + " rest_length = osim.MocoParameter('rest_length',\n", + " '/forceset/passive_knee_device', 'rest_length', osim.MocoBounds(-1, 1))\n", + " problem.addParameter(rest_length)\n", + "\n", + " # Part 5: Update the solver with the new problem (i.e., new model and parameters).\n", + " solver.resetProblem(problem)\n", + " solver.set_optim_max_iterations(500)\n", + "\n", + " # Part 6: Set solver parameter optimization flag.\n", + " # -----------------------------------------------\n", + " # This flag tells MocoCasADiSolver that initSystem() calls on the model are\n", + " # unnecessary to optimize the parameters. Other parameters (e.g., body mass)\n", + " # require initSystem(), since they change the model's underlying multibody\n", + " # system in Simbody. Since changing parameters in SpringGeneralizedForce does\n", + " # not change the multibody system, we can set this flag to false to speed up\n", + " # the optimization.\n", + " solver.set_parameters_require_initsystem(False)\n", + "\n", + " # Part 7: Solve the problem.\n", + " deviceSolutionSealed = study.solve()\n", + " deviceSolution = deviceSolutionSealed.unseal()\n", + " if not os.path.exists('device_optimization'):\n", + " os.mkdir('device_optimization')\n", + " solutionFile = os.path.join('device_optimization',\n", + " 'squat_to_stand_device_optimization_solution.sto')\n", + " deviceSolution.write(solutionFile)\n", + " # study.visualize(deviceSolution)\n", + "\n", + " # Part 8: Print solution information.\n", + " if optimize:\n", + " parameters = deviceSolution.getParameters().to_numpy()\n", + " print(f'Optimized stiffness: {parameters[0]:.2f} Nm/rad')\n", + " print(f'Optimized rest length: {parameters[1]:.2f} rad')\n", + " else:\n", + " print(f'Using stiffness: {stiffness:.2f} Nm/rad')\n", + " print(f'Using rest length: {rest_length:.2f} rad')\n", + " print(f'Muscle effort: {deviceSolution.getObjective():.3f}\\n')\n", + "\n", + " # Part 9: Create a PDF report of the solution.\n", + " output = os.path.join('device_optimization',\n", + " 'squat_to_stand_device_optimization_report.pdf')\n", + " ref_files = []\n", + " ref_files.append(os.path.join('squat_to_stand',\n", + " 'squat_to_stand_prediction_solution.sto'))\n", + " report = osim.report.Report(model,\n", + " solutionFile,\n", + " output=output,\n", + " ref_files=ref_files,\n", + " bilateral=True)\n", + " report.generate()\n", + "\n", + "\n", + "def on_submit(b):\n", + " if mode_selector.value == 'Manual':\n", + " print('Testing manual design...')\n", + " run_device_optimization_squat_to_stand(False,\n", + " stiffness_slider.value, rest_length_slider.value)\n", + " else:\n", + " print('Optimizing design...')\n", + " run_device_optimization_squat_to_stand(True)\n", + "\n", + "# Define layout for consistent width and alignment\n", + "common_layout = widgets.Layout(width='400px')\n", + "\n", + "# Create mode selector dropdown with the common layout\n", + "mode_selector = widgets.Dropdown(\n", + " options=['Manual', 'Optimization'],\n", + " value='Manual',\n", + " description='Design Mode:',\n", + " disabled=False,\n", + " style = {'description_width': 'initial'},\n", + " layout=common_layout,\n", + ")\n", + "\n", + "# Define layout for right-aligning the description and common width for labels\n", + "description_layout = widgets.Layout(display='flex', justify_content='flex-end', width='150px')\n", + "\n", + "# Define slider layout with common width\n", + "slider_layout = widgets.Layout(width='250px')\n", + "\n", + "# Create sliders with the defined layout\n", + "stiffness_slider = widgets.FloatSlider(\n", + " value=0.0,\n", + " min=0.0,\n", + " max=400.0,\n", + " step=0.1,\n", + " disabled=False,\n", + " continuous_update=False,\n", + " orientation='horizontal',\n", + " readout=True,\n", + " readout_format='.1f',\n", + " style={'description_width': 'initial'},\n", + " layout=slider_layout\n", + ")\n", + "\n", + "rest_length_slider = widgets.FloatSlider(\n", + " value=0.0,\n", + " min=-1.0,\n", + " max=1.0,\n", + " step=0.01,\n", + " disabled=False,\n", + " continuous_update=False,\n", + " orientation='horizontal',\n", + " readout=True,\n", + " readout_format='.2f',\n", + " style={'description_width': 'initial'},\n", + " layout=slider_layout\n", + ")\n", + "\n", + "# Create label widgets for alignment\n", + "stiffness_label = widgets.Label(value='stiffness [N-m/rad]:', layout=description_layout)\n", + "rest_length_label = widgets.Label(value='rest length [rad]:', layout=description_layout)\n", + "\n", + "# Combine labels and sliders into HBox\n", + "stiffness_hbox = widgets.HBox([stiffness_label, stiffness_slider], layout=common_layout)\n", + "rest_length_hbox = widgets.HBox([rest_length_label, rest_length_slider], layout=common_layout)\n", + "\n", + "# Create submit button with common layout\n", + "submit_button = widgets.Button(\n", + " description='Submit',\n", + " disabled=False,\n", + " button_style='',\n", + " tooltip='Click to submit',\n", + " icon='check',\n", + " layout=common_layout\n", + ")\n", + "\n", + "# Mode change handler\n", + "def on_mode_change(change):\n", + " if change['new'] == 'Manual':\n", + " stiffness_slider.value = 200.0\n", + " rest_length_slider.value = 0.0\n", + " stiffness_slider.disabled = False\n", + " rest_length_slider.disabled = False\n", + " else:\n", + " stiffness_slider.value = 200.0\n", + " rest_length_slider.value = 0.0\n", + " stiffness_slider.disabled = True\n", + " rest_length_slider.disabled = True\n", + "\n", + "mode_selector.observe(on_mode_change, names='value')\n", + "\n", + "submit_button.on_click(on_submit)\n", + "\n", + "# Display widgets\n", + "display(widgets.VBox([mode_selector, stiffness_hbox, rest_length_hbox, submit_button]))\n", + "\n", + "# Initialize state based on the current mode\n", + "on_mode_change({'new': mode_selector.value})\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8.6. Modeling neural control with muscle synergies\n", + "\n", + "OpenSim now provides support for modeling muscle synergies via the new controller type [`SynergyController`](https://simtk.org/api_docs/opensim/api_docs451/classOpenSim_1_1SynergyController.html). This controller uses OpenSim [`Input`](https://simtk.org/api_docs/opensim/api_docs451/classOpenSim_1_1Input.html#a4d1e8b8a76452dc4d938742f19676fe3)s to pass in control signals from any component in the model that provides a scalar control signal via an [`Output`](https://simtk.org/api_docs/opensim/api_docs451/classOpenSim_1_1Output.html) (e.g., [`SignalGenerator`](https://simtk.org/api_docs/opensim/api_docs451/classOpenSim_1_1SignalGenerator.html)). The input control signals are multiplied by weighting vectors to generate a set of actuator controls. We refer to the input control signals as \"synergy excitations\" and the weighting vectors as \"synergy vectors\". In the following example, the synergies are used to produce muscle exctiations in the model, but `SynergyController` can be used with any OpenSim [`Actuator`](https://simtk.org/api_docs/opensim/api_docs451/classOpenSim_1_1Actuator.html), not just muscles.\n", + "\n", + "![muscle_synergies.png](https://drive.google.com/uc?export=download&id=14c3Ror0dh-beine2KHHyOpCLLD-GDUkc)\n", + "\n", + "Execute the cell below to load an interactive menu for running synergy-driven squat-to-stand motions. The menu provides a slider to select the number of muscle synergies between 1 and 8. As you decrease the number of synergies used by the `SynergyController`, the muscle control strategies will be become increasingly coupled. How will this affect the squat-to-stand motion?\n", + "\n", + "While running the synergy-driven optimizations, consider the following additional questions.\n", + "\n", + "**Question 1. What is the fewest number of synergies where the squat-to-stand motion is still possible?**\n", + "\n", + "**Question 2. Can you incorporate a passive device from the previous section to make standing possible with reduced neural control dimensionality?**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# @title Run a synergy-driven squat-to-stand optimization { display-mode: \"form\" }\n", + "# @markdown Execute the cell to load the menu for specifying the number of synergies.\n", + "\n", + "import ipywidgets as widgets\n", + "from IPython.display import display, Markdown\n", + "import numpy as np\n", + "\n", + "# Suppress scikit-learn warnings.\n", + "import warnings\n", + "from sklearn.exceptions import ConvergenceWarning\n", + "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n", + "\n", + "def printmd(string, color):\n", + " display(Markdown(f'{string}'))\n", + "\n", + "def run_synergy_driven_squat_to_stand(num_synergies):\n", + "\n", + " # Part 1: Load the base squat-to-stand MocoStudy.\n", + " study = osim.MocoStudy(os.path.join('squat_to_stand', 'squat_to_stand.xml'))\n", + " problem = study.updProblem()\n", + " solver = osim.MocoCasADiSolver.safeDownCast(study.updSolver())\n", + "\n", + " # Part 2: Recreate the model, now adding a passive assistive device at the knee\n", + " modelProcessor = osim.ModelProcessor('squatToStand_3dof9musc.osim')\n", + " modelProcessor.append(osim.ModOpScaleMaxIsometricForce(2.0))\n", + " modelProcessor.append(osim.ModOpReplaceMusclesWithDeGrooteFregly2016())\n", + " modelProcessor.append(osim.ModOpIgnorePassiveFiberForcesDGF())\n", + " modelProcessor.append(osim.ModOpIgnoreTendonCompliance())\n", + " modelProcessor.append(osim.ModOpIgnoreActivationDynamics())\n", + " modelProcessor.append(osim.ModOpScaleActiveFiberForceCurveWidthDGF(1.5))\n", + " modelProcessor.append(osim.ModOpReplacePathsWithFunctionBasedPaths(\n", + " os.path.join('path_fitting', 'squatToStand_FunctionBasedPathSet.xml')))\n", + " model = modelProcessor.process()\n", + " model.initSystem()\n", + "\n", + " # Part 3: Load the solution to the squat-to-stand prediction to extract the muscle\n", + " # controls.\n", + " prevSolution = osim.MocoTrajectory(\n", + " os.path.join('squat_to_stand', 'squat_to_stand_prediction_solution.sto'))\n", + " controls = prevSolution.exportToControlsTable()\n", + "\n", + " # Part 4: Use non-negative matrix factorization (NNMF) to extract a set of muscle\n", + " # synergies.\n", + " from sklearn.decomposition import NMF\n", + " nmf = NMF(n_components=num_synergies, init='random', random_state=0, max_iter=1000)\n", + "\n", + " A = controls.getMatrix().to_numpy()\n", + " W = nmf.fit_transform(A)\n", + " H = nmf.components_\n", + "\n", + " # Scale W and H assuming that the elements of H are all 0.5. This prevents the\n", + " # synergy excitations and synergy vector weights from being too small or too large.\n", + " scaleVec = 0.5*np.ones(H.shape[1])\n", + " for i in range(num_synergies):\n", + " scale = np.linalg.norm(scaleVec) / np.linalg.norm(H[i, :])\n", + " H[i, :] *= scale\n", + " W[:, i] /= scale\n", + "\n", + " # Part 5: Add a SynergyController for the model's right leg.\n", + " controller = osim.SynergyController()\n", + " controller.setName(\"synergy_controller\")\n", + " # The number of actuators connected to the controller defines the number of\n", + " # weights in each synergy vector expected by the controller.\n", + " for name in controls.getColumnLabels():\n", + " controller.addActuator(\n", + " osim.Muscle.safeDownCast(model.getComponent(name)))\n", + " # Adding a synergy vector increases the number of synergies in the\n", + " # controller by one. This means that the number of Input control\n", + " # signals expected by the controller is also increased by one.\n", + " for i in range(num_synergies):\n", + " synergyVector = osim.Vector(H.shape[1], 0.0)\n", + " for j in range(H.shape[1]):\n", + " synergyVector.set(j, H[i, j])\n", + " controller.addSynergyVector(synergyVector)\n", + " model.addController(controller)\n", + " model.finalizeConnections()\n", + " model.initSystem()\n", + "\n", + " # Part 6: Update the problem with the new model.\n", + " problem.setModel(model)\n", + "\n", + " # Part 7: Set bounds on the synergy excitations and update the weight on the\n", + " # synergy excitations in the control effort cost term.\n", + " effort = osim.MocoControlGoal.safeDownCast(problem.updGoal(\"control_effort\"))\n", + " # Enabling this flag means muscle excitations are ignored in the effort cost.\n", + " effort.setIgnoreControlledActuators(True)\n", + " for i in range(num_synergies):\n", + " name = f'/controllerset/synergy_controller/synergy_excitation_{i}'\n", + " problem.setInputControlInfo(name, [0, 1.5*np.max(W)])\n", + " effort.setWeightForControl(name, 0.1)\n", + "\n", + " # Part 8: Update the solver with the new problem.\n", + " solver.resetProblem(problem)\n", + "\n", + " # Part 9: Solve!\n", + " solutionSealed = study.solve()\n", + " solution = solutionSealed.unseal()\n", + " if solution.success():\n", + " printmd(f'**Success!** Problem with {num_synergies} synergies converged!',\n", + " 'green')\n", + " else:\n", + " printmd(f'**Failure.** Problem with {num_synergies} synergies did not converge.',\n", + " 'red')\n", + "\n", + " # This function computes the model control values from the\n", + " # SynergyController in the model and inserts them into the solution.\n", + " solution.generateControlsFromModelControllers(model)\n", + " # study.visualize(solution)\n", + "\n", + " # Part 10: Write the solution to a Storage file.\n", + " if not os.path.exists('muscle_synergies'):\n", + " os.mkdir('muscle_synergies')\n", + " solutionFile = os.path.join('muscle_synergies',\n", + " f'squat_to_stand_{num_synergies}_muscle_synergies_solution.sto')\n", + " solution.write(solutionFile)\n", + "\n", + " # Part 11: Create a PDF report of the solution.\n", + " output = os.path.join('muscle_synergies',\n", + " f'squat_to_stand_{num_synergies}_muscle_synergies_solution.pdf')\n", + " ref_files = []\n", + " ref_files.append(os.path.join('squat_to_stand',\n", + " 'squat_to_stand_prediction_solution.sto'))\n", + " report = osim.report.Report(model,\n", + " solutionFile,\n", + " output=output,\n", + " ref_files=ref_files,\n", + " bilateral=True)\n", + " report.generate()\n", + "\n", + "def on_submit(b):\n", + " run_synergy_driven_squat_to_stand(slider.value)\n", + "\n", + "\n", + "common_layout = widgets.Layout(width='300px')\n", + "\n", + "# Define layout for right-aligning the description and common width for labels\n", + "description_layout = widgets.Layout(display='flex', justify_content='flex-end', width='100px')\n", + "\n", + "# Define slider layout with common width\n", + "slider_layout = widgets.Layout(width='250px')\n", + "\n", + "slider = widgets.IntSlider(\n", + " value=5,\n", + " min=1,\n", + " max=8,\n", + " step=1,\n", + " disabled=False,\n", + " continuous_update=False,\n", + " orientation='horizontal',\n", + " readout=True,\n", + " # readout_format='.1f',\n", + " style={'description_width': 'initial'},\n", + " layout=slider_layout\n", + ")\n", + "\n", + "# Create label widgets for alignment\n", + "slider_label = widgets.Label(value='no. synergies:', layout=description_layout)\n", + "\n", + "# Combine labels and sliders into HBox\n", + "slider_hbox = widgets.HBox([slider_label, slider], layout=common_layout)\n", + "\n", + "submit_button = widgets.Button(\n", + " description='Submit',\n", + " disabled=False,\n", + " button_style='',\n", + " tooltip='Click to submit',\n", + " icon='check',\n", + " layout=common_layout\n", + ")\n", + "\n", + "submit_button.on_click(on_submit)\n", + "\n", + "display(widgets.VBox([slider_hbox, submit_button]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J5mhS9zNX9ff" + }, + "source": [ + "## 8.7. Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_M04jPJLuiUz" + }, + "source": [ + "In this tutorial you used [condacolab](https://github.com/conda-incubator/condacolab) to install [Miniconda](https://docs.conda.io/en/latest/miniconda.html) in [Google Colab](https://colab.research.google.com/?hl=en). Then, you installed Conda [OpenSim](https://opensim.stanford.edu/) with Moco into the Miniconda environment. You downloaded the 3 DOF, 9 muscle \"squat-to-stand\" model, fit function-based muscle paths, and used trajectory optimization to predict moving from a squat to a stand. Finally, you designed parameters for a passive assistive device at the knee and used muscle synergies to model how neural control limitations affect the squat-to-stand motion." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lLWgd8Noun6Y" + }, + "source": [ + "## 8.8. Useful Links" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m8M3dlT5xQcD" + }, + "source": [ + "> **OpenSim Website:** https://opensim.stanford.edu/\n", + ">\n", + "> **OpenSim Python Scripting:** https://opensimconfluence.atlassian.net/wiki/spaces/OpenSim/pages/53085346/Scripting+in+Python\n", + ">\n", + "> **OpenSim Moco Website:** https://opensim-org.github.io/opensim-moco-site/\n", + ">\n", + "> **OpenSim Moco Publication:** https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008493\n", + ">\n", + "> **OpenSim SimTK Website:** https://simtk.org/projects/opensim" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rFR2Rf9tk_9A" + }, + "source": [ + "## 8.9. Acknowledgments\n", + "\n", + "Thanks to [OpenSimColab](https://simtk.org/projects/opencolab) project [[2]](https://doi.org/10.1080/10255842.2022.2104607) for creating the first OpenSim Conda package." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dxMjdukAnH2N" + }, + "source": [ + "## 8.10. References" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WX2A_iaYulV1" + }, + "source": [ + "> [1] Dembia CL, Bianco NA, Falisse A, Hicks JL, Delp SL (2020) **OpenSim Moco: Musculoskeletal optimal control.** PLOS Computational Biology 16(12): e1008493. https://doi.org/10.1371/journal.pcbi.1008493\n", + ">\n", + "> [2] Mokhtarzadeh, H., Jiang, F., Zhao, S., & Malekipour, F. (2022). **OpenColab project: OpenSim in Google colaboratory to explore biomechanics on the web.** *Computer Methods in Biomechanics and Biomedical Engineering*, 1–9. https://doi.org/10.1080/10255842.2022.2104607" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "opensim", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Bindings/Python/tutorials/resources/Tutorial 3/double_pendulum.osim b/Bindings/Python/tutorials/resources/Tutorial 3/double_pendulum.osim new file mode 100644 index 0000000000..c0009a0b44 --- /dev/null +++ b/Bindings/Python/tutorials/resources/Tutorial 3/double_pendulum.osim @@ -0,0 +1,275 @@ + + + + + 0 -9.80665 0 + + Ajay Seth + + For testing + + meters + + N + + + + + + 1 + + 0 0.2 0 + + 0 0 0 0 0 0 + + + + + + + + + + + + + cylinder.vtp + + 1 1 1 + + + + 0 0 0 0 0.25 0 + + 0.02 0.5 0.02 + + 4 + + 1 + + + + sphere.vtp + + 1 1 1 + + + + 0 0 0 0 0 0 + + 0.1 0.1 0.1 + + 4 + + 1 + + + + + + 1 1 1 + + 0 0 0 0 0 0 + + true + + 4 + + + + + 1 + + 0 0 0 + + 0 0 0 0 0 0 + + + + + + + + + + + + + cylinder.vtp + + 1 1 1 + + + + 0 0 0 0 0.25 0 + + 0.02 0.5 0.02 + + 4 + + 1 + + + + sphere.vtp + + 1 1 1 + + + + 0 0 0 0 0 0 + + 0.1 0.1 0.1 + + 4 + + 1 + + + + + + 1 1 1 + + 0 0 0 0 0 0 + + true + + 4 + + + + + + + + + + + + + + + + + + + + ../ground + + + + ../rod1 + + + + 0 0 0 + + 0 0 0 + + 0 0.5 0 + + 0 0 0 + + + + + + rotational + + -0.78539816 + + 0 + + -1.57079633 1.57079633 + + false + + false + + + + false + + + + + + false + + + + + + + rod1 + + + + rod2 + + + + 0 0 0 + + 0 0 0 + + 0 0.5 0 + + 0 0 0 + + + + + + rotational + + -1.04719755 + + 0 + + -1.57079633 1.57079633 + + false + + false + + + + false + + + + + + false + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Bindings/Python/tutorials/resources/Tutorial 4/crouch_gait.mot b/Bindings/Python/tutorials/resources/Tutorial 4/crouch_gait.mot new file mode 100644 index 0000000000..5d64a0a370 --- /dev/null +++ b/Bindings/Python/tutorials/resources/Tutorial 4/crouch_gait.mot @@ -0,0 +1,126 @@ +crouch1_gait +version=1 +nRows=120 +nColumns=18 +inDegrees=yes +endheader +time pelvis_list pelvis_rotation pelvis_tilt hip_flexion_r hip_adduction_r hip_rotation_r knee_angle_r ankle_angle_r hip_flexion_l hip_adduction_l hip_rotation_l knee_angle_l ankle_angle_l pelvis_ty lumbar_bending lumbar_rotation lumbar_extension +0 -5.9 4.71 -4.02 53.96 11.33 15.56 -59.26 13.07 32.67 -2.43 16.66 -73.54 28.22 0.82 5.9 -4.71 -15.98 +0.008474575 -5.925 5.065 -4.16 54.03 12.045 14.97 -60.435 14.015 32.25 -2.665 16.425 -74.335 28.06 0.82 5.925 -5.065 -15.84 +0.01694915 -5.95 5.42 -4.3 54.1 12.76 14.38 -61.61 14.96 31.83 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-1.28 53.47 10.02 13.4 -57.64 13.55 33.94 -2.35 14.77 -67.85 28.61 0.82 8.09 -5.87 -18.72 +0.974576125 -7.955 6.275 -1.555 53.515 10.7 14.06 -57.905 13.385 33.68 -2.25 15.31 -68.805 28.555 0.82 7.955 -6.275 -18.445 +0.9830507 -7.82 6.68 -1.83 53.56 11.38 14.72 -58.17 13.22 33.42 -2.15 15.85 -69.76 28.5 0.82 7.82 -6.68 -18.17 +0.99152535 -7.56 7.26 -2.215 53.765 11.7 14.74 -59.105 13.56 33.275 -2.18 16.24 -70.72 28.49 0.82 7.56 -7.26 -17.785 +1 -7.3 7.84 -2.6 53.97 12.02 14.76 -60.04 13.9 33.13 -2.21 16.63 -71.68 28.48 0.82 7.3 -7.84 -17.4 diff --git a/Bindings/Python/tutorials/resources/Tutorial 4/gait2392.osim b/Bindings/Python/tutorials/resources/Tutorial 4/gait2392.osim new file mode 100644 index 0000000000..5f31e67902 --- /dev/null +++ b/Bindings/Python/tutorials/resources/Tutorial 4/gait2392.osim @@ -0,0 +1,11664 @@ + + + + Delp S.L., Loan J.P., Hoy M.G., Zajac F.E., Topp E.L., Rosen J.M., Thelen D.G., Anderson F.C., Seth A. Notes: 3D, 23 DOF gait model created by D.G. Thelen, Univ. of Wisconsin-Madison, and Ajay Seth, Frank C. Anderson, and Scott L. Delp, Stanford University. Lower extremity joint defintions based on Delp et al. (1990). Low back joint and anthropometry based on Anderson and Pandy (1999, 2001). Planar knee model of Yamaguchi and Zajac (1989). Seth replaced tibia translation constraints with a CustomJoint for the knee and removed the patella to eliminate all kinematic constraints; insertions of the quadrucepts are handled with moving points in the tibia frame as defined by Delp 1990. License: Creative Commons (CCBY 3.0). You are free to distribute, remix, tweak, and build upon this work, even commercially, as long as you credit us for the original creation. http://creativecommons.org/licenses/by/3.0/ More Information: http://simtk-confluence.stanford.edu:8080/display/OpenSim/Gait+2392+and+2354+Models + Delp, S.L., Loan, J.P., Hoy, M.G., Zajac, F.E., Topp E.L., Rosen, J.M.: An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures, IEEE Transactions on Biomedical Engineering, vol. 37, pp. 757-767, 1990. Yamaguchi G.T., Zajac F.E.: A planar model of the knee joint to characterize the knee extensor mechanism." J . Biomecl7. vol. 21. pp. 1-10. 1989. Anderson F.C., Pandy M.G.: A dynamic optimization solution for vertical jumping in three dimensions. Computer Methods in Biomechanics and Biomedical Engineering 2:201-231, 1999. Anderson F.C., Pandy M.G.: Dynamic optimization of human walking. 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D.G. Thelen, Univ. of Wisconsin-Madison, and Ajay Seth, + Frank C. Anderson, and Scott L. Delp, Stanford University. Lower extremity joint defintions based on Delp et al. (1990). + Low back joint and anthropometry based on Anderson and Pandy (1999, 2001). Planar knee model of Yamaguchi and Zajac (1989). + Seth replaced tibia translation constraints with a CustomJoint for the knee and removed the patella to eliminate all kinematic constraints; insertions of the quadrucepts are handled with moving points in the tibia frame as defined by Delp 1990. + Number of muscles was reduced by Anderson to improve simulation speed for demonstrations and is not intended to be used in research. + + License: + Creative Commons (CCBY 3.0). You are free to distribute, remix, tweak, and build upon this work, even commercially, + as long as you credit us for the original creation. + http://creativecommons.org/licenses/by/3.0/ + + More Information: + http://simtk-confluence.stanford.edu:8080/display/OpenSim/Gait+2392+and+2354+Models + + + Delp, S.L., Loan, J.P., Hoy, M.G., Zajac, F.E., Topp E.L., Rosen, J.M.: An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures, IEEE Transactions on Biomedical Engineering, vol. 37, pp. 757-767, 1990. + Yamaguchi G.T., Zajac F.E.: A planar model of the knee joint to characterize the knee extensor mechanism." J . Biomech. vol. 22. pp. 1-10. 1989. + Anderson F.C., Pandy M.G.: A dynamic optimization solution for vertical jumping in three dimensions. Computer Methods in Biomechanics and Biomedical Engineering 2:201-231, 1999. + Anderson F.C., Pandy M.G.: Dynamic optimization of human walking. 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0.99444594 diff --git a/Bindings/Python/tutorials/resources/Tutorial 5/subject01_walk1_grf.xml b/Bindings/Python/tutorials/resources/Tutorial 5/subject01_walk1_grf.xml new file mode 100644 index 0000000000..1fad3903be --- /dev/null +++ b/Bindings/Python/tutorials/resources/Tutorial 5/subject01_walk1_grf.xml @@ -0,0 +1,52 @@ + + + + + + false + + calcn_r + + ground + + ground + + ground_force_v + + ground_force_p + + ground_torque_ + + Unassigned + + + false + + calcn_l + + ground + + ground + + 1_ground_force_v + + 1_ground_force_p + + 1_ground_torque_ + + Unassigned + + + + + subject01_walk1_grf.mot + + diff --git a/Bindings/Python/tutorials/resources/Tutorial 6/gait10dof18musc_Strong_actuators.xml b/Bindings/Python/tutorials/resources/Tutorial 6/gait10dof18musc_Strong_actuators.xml new file mode 100644 index 0000000000..aa726193a3 --- /dev/null +++ b/Bindings/Python/tutorials/resources/Tutorial 6/gait10dof18musc_Strong_actuators.xml @@ -0,0 +1,157 @@ + + + + + + + + false + + -Inf + + Inf + + pelvis + + -0.034 0.0 0 + + false + + 1 0 0 + + true + + 100 + + + + false + + -Inf + + Inf + + pelvis + + -0.034 0.0 0 + + false + + 0 1 0 + + true + + 100 + + + + false + + -Inf + + Inf + + pelvis + + ground + + true + + 0 0 1 + + 100 + + + + + + false + + -Inf + + Inf + + hip_flexion_r + + 100 + + + + false + + -Inf + + Inf + + knee_angle_r + + 100 + + + + false + + -Inf + + Inf + + ankle_angle_r + + 100 + + + + false + + -Inf + + Inf + + hip_flexion_l + + 100 + + + + false + + -Inf + + Inf + + knee_angle_l + + 100 + + + + false + + -Inf + + Inf + + ankle_angle_l + + 100 + + + + false + + -Inf + + Inf + + lumbar_extension + + 100 + + + + + diff --git a/Bindings/Python/tutorials/resources/Tutorial 6/gait10dof18musc_simbody.osim b/Bindings/Python/tutorials/resources/Tutorial 6/gait10dof18musc_simbody.osim new file mode 100644 index 0000000000..720c2398b6 --- /dev/null +++ b/Bindings/Python/tutorials/resources/Tutorial 6/gait10dof18musc_simbody.osim @@ -0,0 +1,3695 @@ + + + + A planar reduced gait model with 10 degrees of freedom and 18 muscles by Ajay Seth. Closely based on Delp S.L., Loan J.P., Hoy M.G., Zajac F.E., Topp E.L., Rosen J.M., Thelen D.G., Anderson F.C., Seth A. Notes: 3D, 23 DOF gait model created by D.G. Thelen, Univ. of Wisconsin-Madison, and Ajay Seth, Frank C. Anderson, and Scott L. Delp, Stanford University. Lower extremity joint defintions based on Delp et al. (1990). Low back joint and anthropometry based on Anderson and Pandy (1999, 2001). Planar knee model of Yamaguchi and Zajac (1989). Seth replaced tibia translation constraints with a CustomJoint for the knee and removed the patella to eliminate all kinematic constraints; insertions of the quadrucepts are handled with moving points in the tibia frame as defined by Delp 1990. Number of muscles was reduced by Anderson to improve simulation speed for demonstrations and is not intended to be used in research. License: Creative Commons (CCBY 3.0). You are free to distribute, remix, tweak, and build upon this work, even commercially, as long as you credit us for the original creation. http://creativecommons.org/licenses/by/3.0/ More Information: http://simtk-confluence.stanford.edu:8080/display/OpenSim/Gait+2392+and+2354+Models + Delp, S.L., Loan, J.P., Hoy, M.G., Zajac, F.E., Topp E.L., Rosen, J.M.: An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures, IEEE Transactions on Biomedical Engineering, vol. 37, pp. 757-767, 1990. Yamaguchi G.T., Zajac F.E.: A planar model of the knee joint to characterize the knee extensor mechanism." J . Biomech. vol. 22. pp. 1-10. 1989. Anderson F.C., Pandy M.G.: A dynamic optimization solution for vertical jumping in three dimensions. Computer Methods in Biomechanics and Biomedical Engineering 2:201-231, 1999. Anderson F.C., Pandy M.G.: Dynamic optimization of human walking. 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a/CHANGELOG.md b/CHANGELOG.md index e4f43602c9..95ecfc75f7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -23,6 +23,8 @@ v4.6 - Added an option to record a full `StatesTrajectory` (via `Manager::setRecordStatesTrajectory`). - Support for user-specified time step sequences (e.g., `setUseSpecifiedDT`, `useContactDT`, etc.) has been removed. - Convenience methods for a few advanced `SimTK::Integrator` settings have been added (e.g., `Manager::setIntegratorFinalTime`). +- Added a suite of Jupyter notebooks containing OpenSim API Python tutorials. (#4124) + v4.5.2