Movement Tracking MuJoCo is a cutting-edge project that harnesses the power of MuJoCo physics simulation combined with machine learning to create an intelligent humanoid movement analysis system. This project seamlessly integrates:

• Advanced Physics Simulation using MuJoCo engine
• LSTM-based Motion Prediction for movement forecasting
• Real-time Visualization with interactive React frontend
• Motion Tracking & Analysis for biomechanical research

Perfect for researchers in robotics, biomechanics, machine learning, and anyone interested in human movement simulation and prediction.

Table of Contents

• Overview
• Features
• Requirements
• Installation
• Usage
• Project Structure
• Examples
• Contributing
• License
• Contact

Overview

This project simulates a humanoid model (e.g., humanoid.xml) using MuJoCo, tracks its motion, and employs an LSTM model to predict or analyze movement sequences. A React-based GUI allows users to interact with the simulation, load data, train models, and visualize results in real-time or post-processed formats.

✨ Key Features

🤖 Simulation	🧠 AI/ML	🎨 Visualization	🔧 Development
Realistic Physics	LSTM Networks	Real-time Rendering	Modular Design
MuJoCo-powered humanoid simulation	Motion prediction & forecasting	Interactive 3D visualization	Extensible architecture
Motion Capture	Data Processing	Web Interface	Easy Integration
Joint tracking & analysis	CSV data handling	React-based GUI	RESTful API endpoints

🎯 Core Capabilities

• 🏃‍♂️ Advanced Humanoid Simulation

  • Realistic movement simulation using MuJoCo physics engine
  • Support for complex humanoid models and custom XML configurations
  • Real-time physics calculations with accurate collision detection

• 📊 Intelligent Motion Tracking

  • Captures joint angles, velocities, accelerations, and positions
  • Processes and analyzes biomechanical data
  • Exports motion data to CSV format for further analysis

• 🧠 LSTM-based Prediction

  • Deep learning models for motion sequence prediction
  • Time-series analysis of movement patterns
  • Customizable neural network architectures

• 🎨 Interactive Visualization

  • Modern React-based web interface
  • Real-time 3D simulation rendering
  • Matplotlib integration for data plotting
  • Responsive design for all devices

• 🔬 Research-Ready Platform

  • Modular design for easy customization
  • Support for custom sensors and algorithms
  • Comprehensive data export capabilities
  • Integration with popular ML frameworks

📋 Prerequisites

Component	Version	Purpose
	3.7+	Backend processing & ML
	16+	Frontend development
	Latest	Physics simulation

🔧 System Dependencies

• MuJoCo Physics Engine - Installation Guide
• Python Dependencies - Listed in backend/requirements.txt
• Node.js Dependencies - Listed in frontend/package.json

📦 Key Python Packages

tensorflow>=2.0    # Deep learning framework
numpy>=1.19        # Numerical computing
flask>=2.0         # Web framework
matplotlib>=3.3    # Data visualization
pandas>=1.2        # Data manipulation
mujoco>=2.0        # MuJoCo Python bindings

⚡ Quick Setup

🚀 One-Click Installation

# Clone the repository
git clone https://github.com/17arhaan/Movement_Tracking_Mujoco.git
cd Movement_Tracking_Mujoco

# Install everything at once
chmod +x setup.sh && ./setup.sh

📝 Manual Installation

🐍 Backend Setup (Python)

# 1. Create virtual environment
python -m venv venv

# 2. Activate virtual environment
# On macOS/Linux:
source venv/bin/activate
# On Windows:
# venv\Scripts\activate

# 3. Install MuJoCo (follow official guide)
# https://mujoco.org/docs/installation.html

# 4. Install Python dependencies
pip install -r backend/requirements.txt

# 5. Verify installation
python backend/mujoco_sim.py

⚛️ Frontend Setup (React)

# 1. Navigate to frontend directory
cd frontend

# 2. Install Node.js dependencies
npm install

# 3. Start development server
npm start

# 4. Open browser to http://localhost:3000

🔍 Verification Steps

1. Check MuJoCo Installation:

   python -c "import mujoco; print('MuJoCo installed successfully!')"

2. Verify Backend:

   cd backend && python api.py
   # Should start Flask server on http://localhost:5000

3. Test Frontend:

   cd frontend && npm start
   # Should open React app on http://localhost:3000

4. Ensure Model Files:

   • Place humanoid.xml in backend/ directory
   • Or adjust model paths in mujoco_sim.py

🎮 Usage Guide

🚀 Getting Started (3 Simple Steps)

Step	Action	Command	Result
1	🔧 Start Backend	cd backend && python api.py	Flask server at localhost:5000
2	🎨 Launch Frontend	cd frontend && npm start	React app at localhost:3000
3	🎉 Start Exploring	Open browser	Interactive simulation interface

---

🖥️ Detailed Usage

Running the Backend

Launch the Flask API to manage simulation and LSTM processing:

cd backend
python api.py

The server runs at http://localhost:5000.

Running the Frontend

Start the React GUI:

cd frontend
npm start

Visit http://localhost:3000 to access the interface.

Running Standalone Simulation (Optional)

To run a standalone MuJoCo simulation without the GUI:

cd backend
python mujoco_sim.py

This renders the humanoid model with dummy joint angles.

Training the LSTM Model

The GUI’s "Train LSTM" button triggers training via the API. Alternatively, run standalone:

cd backend
python lstm_model.py

Modify lstm_model.py to load your dataset.

Visualization

The GUI’s "Simulate Motion" button renders MuJoCo output. For standalone plots:

cd backend
python motion_tracker.py  # Add Matplotlib plotting logic here

🏗️ Project Architecture

graph TB
    subgraph Frontend["🎨 Frontend (React)"]
        UI[Web Interface]
        Controls[Motion Controls]
        Viz[3D Visualizer]
    end
    
    subgraph Backend["🔧 Backend (Python)"]
        API[Flask API]
        Sim[MuJoCo Simulator]
        LSTM[LSTM Models]
        Tracker[Motion Tracker]
    end
    
    subgraph Data["📊 Data Layer"]
        XML[XML Models]
        CSV[Motion Data]
        Models[Trained Models]
    end
    
    UI --> API
    Controls --> Sim
    Viz --> Tracker
    API --> LSTM
    Sim --> XML
    Tracker --> CSV
    LSTM --> Models

Loading

📁 Directory Structure

Movement_Tracking_Mujoco/
├── 🔧 backend/                        # Python backend ecosystem
│   ├── 🎯 api.py                     # Flask RESTful API server
│   ├── 🤖 mujoco_sim.py              # MuJoCo physics simulation engine
│   ├── 🧠 lstm_model.py              # Deep learning model architecture
│   ├── 📊 motion_tracker.py          # Motion capture & analysis
│   ├── 📋 requirements.txt           # Python dependency specifications
│   └── 🗂️ models/                   # Trained model storage
│
├── 🎨 frontend/                       # React web application
│   ├── 📱 src/
│   │   ├── ⚛️ App.js                # Main application component
│   │   ├── 🎮 MotionControl.js       # Simulation control interface
│   │   ├── 🖼️ Visualizer.js         # Real-time visualization
│   │   ├── 🔗 index.js              # Application entry point
│   │   └── 🎨 styles/               # CSS styling
│   ├── 🌐 public/
│   │   ├── 📄 index.html            # HTML template
│   │   └── 🖼️ assets/              # Static assets
│   ├── 📦 package.json              # Node.js dependencies
│   └── 🔧 build/                    # Production build output
│
├── 🤖 models/                         # MuJoCo model definitions
│   ├── 🚶 humanoid.xml             # Human biomechanical model
│   ├── 🎯 environments/             # Custom environments
│   └── 🔧 configs/                  # Model configurations
│
├── 📊 data/                          # Training & simulation data
│   ├── 📈 training/                 # LSTM training datasets
│   ├── 📉 results/                  # Simulation outputs
│   └── 🔄 logs/                     # System logs
│
├── 🧪 test_cases/                    # Development test scenarios
│   ├── 🧪 test1/                    # Basic simulation tests
│   ├── 🧪 test2/                    # Advanced ML workflows
│   └── 🧪 test3/                    # Integration tests
│
├── 📚 docs/                          # Documentation
│   ├── 📖 api.md                    # API documentation
│   ├── 🔬 research.md               # Research methodology
│   └── 🎯 tutorials/                # User tutorials
│
├── 🛠️ scripts/                       # Utility scripts
│   ├── 🚀 setup.sh                 # Automated setup
│   ├── 🧹 cleanup.py               # Data preprocessing
│   └── 📊 analysis.py               # Data analysis tools
│
├── 📄 LICENSE                        # MIT License
├── 📖 README.md                      # This file
└── ⚙️ .gitignore                    # Git ignore patterns

🔄 Data Flow

1. 🌐 User Interaction → React frontend captures user inputs
2. 📡 API Communication → Frontend sends requests to Flask backend
3. 🤖 Physics Simulation → MuJoCo processes movement calculations
4. 📊 Data Processing → Motion tracker analyzes simulation output
5. 🧠 ML Prediction → LSTM models process temporal sequences
6. 🎨 Visualization → Results rendered in real-time interface

Examples

Example 1: Simulate Random Motion

• Start the backend (python backend/api.py).
• Open the GUI (npm start in frontend/).
• Click "Simulate Motion" to render a random humanoid pose.

Example 2: Train LSTM on Dummy Data

• Run python backend/lstm_model.py standalone to train on random sequences.
• Check the console for training progress and saved model (lstm_model.h5).

Example 3: Visualize Motion Data

• Modify motion_tracker.py to output joint angles to a CSV.
• Use Matplotlib in a custom script (e.g., visualize.py):

  import matplotlib.pyplot as plt
  import pandas as pd
  data = pd.read_csv("motion_data.csv")
  plt.plot(data["time"], data["joint_1"])
  plt.show()

Contributing

Contributions are encouraged! To contribute:

1. Fork the repository.
2. Create a branch (git checkout -b feature/your-feature).
3. Commit changes (git commit -m "Add your feature").
4. Push to the branch (git push origin feature/your-feature).
5. Submit a pull request.

For significant changes, open an issue first. Adhere to the project’s coding style and document new features.

🚀 Built with passion for advancing human movement simulation

Made with ❤️ by Arhaan