Running Analysis Project

Overview

This project provides a real-time running analysis tool using computer vision and pose estimation techniques. It captures video input from a camera, analyzes the runner's form, and provides instant feedback on various biomechanical metrics.

Features

• Real-time pose estimation using multiple model options (MoveNet, BlazePose, Lite-HRNet)
• Calculation of key running metrics (e.g., head angle, trunk angle, knee angles, arm swing)
• Visual feedback with adjustable display modes (angles, metrics, recommendations)
• Audio feedback for immediate form corrections
• Metric logging for post-run analysis
• Video recording and playback functionality

Requirements

• Python 3.7+
• OpenCV
• TensorFlow
• PyTorch
• MediaPipe
• PyQt5
• NumPy
• pyttsx3

Installation

1. Clone the repository:

   git clone https://github.com/your-username/running-analysis.git
   cd running-analysis
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Download the necessary model files and place them in the models/ directory.

Usage

Run the main script with desired options:

python main.py --model_type blazepose --side left --runner_height 182

Options:

• --model_type: Choose from 'movenet', 'blazepose', or 'lite_hrnet' (default: blazepose)
• --side: 'left' or 'right', depending on which side of the runner is facing the camera (default: left)
• --runner_height: Height of the runner in cm (default: 182)

Key Components

• core/analyzer.py: Main analysis loop and frame processing
• models/: Contains different pose estimation model implementations
• metrics/: Calculates various running metrics
• visualization/: Handles display of metrics and pose on video frames
• feedback/: Provides recommendations and audio feedback
• utils/: Utility functions for angle calculation, filtering, etc.

Customization

• Adjust thresholds in feedback/assessment_calculator.py to customize metric assessments
• Modify feedback/recommendations.py to change the recommendation logic
• Update core/config.py for global configuration settings

Post-Run Analysis

After each session:

1. Options to review recorded video
2. View summary of logged metrics
3. Save or delete session data

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgements

• TensorFlow for MoveNet implementation
• Google's MediaPipe for BlazePose
• Contributors to the Lite-HRNet project