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Biomimetic Hand using InMoov, MG996R Servos & Arduino Uno

This project implements a low-cost biomimetic robotic hand that replicates human finger movements using surface EMG (Electromyography) signals. The system is based on an InMoov 3D printed hand, actuated by MG996R servo motors, and controlled by an Arduino Uno. Muscle activity obtained from EMG sensors is used to detect user intent and map them to hand gestures such as grasping.

WhatsApp Image 2025-10-03 at 11 33 18_5f441f6f


Features

  • 3D Printed Mechanism: Utilizes InMoov hand designs for realistic anthropomorphic structure.
  • Actuation: Controlled by 5 MG996R servo motors for finger flexion and grip.
  • Control Board: Arduino Uno as the controller for handling EMG signals and servo actuation.
  • Sensing: EMG sensors detect muscle contractions and transmit signals for processing.
  • Gesture Control: Enables different gesture sets (e.g., open hand, fist).
  • Low-Cost & Open Source: Made from readily available 3D models and components.

Hardware Components

  • Arduino Uno
  • MG996R Servo Motors 5
  • EMG Sensors (MyoWare Muscle Sensor)
  • 3D Printed InMoov Hand components
  • Power supply (separate supply for servos, 5–6V, 2A+)
  • Breadboard, wires, and connectors

Software Requirements

  • Arduino IDE (2.0+)
  • Servo library for Arduino (included by default)

Wiring & Connections

  • Each MG996R servo is connected to Arduino PWM pins (e.g., D3–D9).
  • Servo Vcc power line connected to external 5–6V power supply.
  • Common ground between Arduino and power supply is mandatory.
  • EMG sensor output connected to Arduino analog input pin (e.g., A0).
  • Additional EMG sensors can control multiple fingers for advanced mapping.

Basic Arduino Control Flow

  1. Read EMG signal from the sensor.
  2. Apply threshold filtering to detect muscle activation.
  3. Map activation state to a predefined finger/gesture motion.
  4. Generate PWM signals to control MG996R servos.

Applications

  • Prosthetic research
  • Rehabilitation robotics
  • Gesture-controlled robotic hand
  • Educational biomimicry & biorobotics projects

Future Improvements

  • Add multiple EMG channels for individual finger control.
  • Implement adaptive filtering for more robust EMG signal interpretation.
  • Integrate with ROS2 for advanced robotic frameworks.
  • Explore tendon-driven actuation instead of direct servo linkage for smoother motion.

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