EmoBrace

EmoBrace is a wearable device project designed to monitor physiological signals and predict the user's emotional state. By leveraging sensor data and machine learning, EmoBrace provides real-time emotion detection and personalized suggestions to improve emotional well-being.

Features

• Emotion Detection: Predicts emotions such as Happy, Sad, Neutral, Angry, Anxious, and Stressed using physiological signals.
• Sensor Integration: Reads data from:
  • LM35 Temperature Sensor
  • AD8232 ECG Sensor
  • GSR Sensor
• Machine Learning: Utilizes a pre-trained model (final_emotion_model.pkl) and a scaler (scaler.pkl) for emotion prediction.
• Data Collection: Collects and stores sensor data for analysis and training.
• Firebase Integration: Uploads processed data and predictions to Firestore for real-time storage and analysis.

Project Structure

.gitignore
capstone.py
data_collection.py
google-services.json
ml_prediction.py
serviceAccountKey.json

File Descriptions

• capstone.py: Main script for real-time data collection, emotion prediction, and uploading results to Firestore.
• data_collection.py: Script for collecting labeled sensor data and saving it to a CSV file for training purposes.
• ml_prediction.py: Script for testing the emotion prediction model with live sensor data.
• serviceAccountKey.json: Firebase service account key for authentication (ignored by .gitignore).
• google-services.json: Firebase configuration file (ignored by .gitignore).
• .gitignore: Specifies files to exclude from version control.

Prerequisites

1. Hardware:

   • LM35 Temperature Sensor
   • AD8232 ECG Sensor
   • GSR Sensor
   • ADS1115 ADC Module
   • Raspberry Pi or compatible microcontroller with I2C support

2. Software:

   • Python 3.x
   • Required Python libraries (install via pip):

     pip install numpy pandas joblib adafruit-circuitpython-ads1x15 firebase-admin google-cloud-firestore
     

3. Firebase Setup:

   • Create a Firebase project.
   • Download the serviceAccountKey.json and google-services.json files and place them in the project directory.

4. Machine Learning Model:

   • Ensure final_emotion_model.pkl and scaler.pkl are in the project directory.

Hardware Design

Block Diagram

Schematic Diagram

Wiring Diagram

Usage

1. Real-Time Emotion Detection

Run the main script to collect sensor data, predict emotions, and upload results to Firestore:

python capstone.py

2. Data Collection for Training

Collect labeled sensor data for training the emotion prediction model:

python data_collection.py

3. Test Emotion Prediction

Test the pre-trained model with live sensor data:

python ml_prediction.py

Emotion Labels and Suggestions

Emotion	Suggestion
Happy	Keep enjoying the moment!
Sad	Take some time to relax and focus on yourself.
Neutral	Maintain your calm and steady approach.
Angry	Take deep breaths and try stepping away.
Anxious	Focus on your breathing and grounding techniques.
Stressed	Consider taking short breaks to decompress.

Notes

• Ensure all hardware components are properly connected before running the scripts.
• The serviceAccountKey.json and google-services.json files are sensitive and should not be shared publicly.

Acknowledgments

• Adafruit for the ADS1115 library.
• Firebase for real-time database services.
• Scikit-learn for machine learning tools.## Emotion Labels and Suggestions