AI-fitness-trainer

The AI Fitness Trainer is a Python-based application designed to assist users in monitoring and improving their exercise form using real-time pose detection. This project utilizes computer vision techniques with OpenCV and MediaPipe to track body movements during various exercises, providing feedback on performance and counting repetitions.

Features

• Real-time Pose Detection: Utilizes MediaPipe to detect body landmarks and calculate angles for various exercises.

• Exercise Tracking: Supports multiple exercises including push-ups, squats, jumping jacks, sit-ups, and lunges, with real-time feedback on form and repetitions.

• User Interface: A simple graphical user interface (GUI) built with Tkinter for exercise selection and control.

• Visual Feedback: Provides visual cues for maintaining proper form and alignment during exercises.

Requirements

To run this project, ensure you have the following installed:

• Python 3.x
• OpenCV
• NumPy
• MediaPipe
• Tkinter (usually included with Python installations)

You can install the required packages using pip:

pip install opencv-python numpy mediapipe

File Structure

The project consists of two main files:

1. camera4.py: The main application file that handles video capture, pose detection, exercise logic, and the GUI.

2. PoseModule.py: A module that encapsulates the pose detection functionality using MediaPipe.

Usage

Running the Application

1. Ensure your webcam is connected and accessible.

2. Run the main application file:

   python camera4.py

3. Upon launching, a GUI will appear allowing you to select an exercise from the dropdown menu.

4. Click the "Start Exercise" button to begin tracking your selected exercise. The application will provide real-time feedback on your performance.

5. Press 'q' to quit the exercise session or use the "Quit" button in the GUI.

Supported Exercises

The application currently supports the following exercises:

• Push-ups
• Squats
• Jumping Jacks
• Sit-ups
• Lunges

Each exercise has specific criteria for counting repetitions based on joint angles and body alignment.

Functionality Detail

Pose Detection

The PoseModule.py file contains a class poseDetector that implements methods for:

• Finding poses in images.
• Extracting landmark positions.
• Calculating angles between specified body points.

The angles are crucial for determining if the user is performing exercises correctly.

Exercise Logic

In camera4.py, each exercise has its own logic function (e.g., push_up_logic, squats_logic, etc.) that:

• Calculates necessary angles for proper form.
• Counts repetitions based on angle thresholds.
• Provides visual feedback on performance through progress bars and text messages displayed on the video feed.

GUI Implementation

The Tkinter-based GUI allows users to:

• Select an exercise from a dropdown menu.
• Start or quit the exercise session easily.

Contribution

Contributions are welcome! If you would like to contribute to this project, please fork the repository and submit a pull request with your changes.

License

This project is licensed under the MIT License - see the LICENSE file for details.