AI-Powered Table Tennis Coaching Platform

PaddleCoach is an innovative web application that leverages artificial intelligence to revolutionize table tennis training. From beginners to professionals, our platform provides cutting-edge tools to analyze performance, track progress, and elevate your game.

Track every ball movement with precision. Visualize your shot patterns with heat maps to identify strengths and weaknesses in your game strategy.

AI-powered body tracking captures your movements, posture, and technique. Get instant feedback on your form and positioning during play.

Your personal AI coach guides you through training with voice commands and real-time feedback. Train smarter with intelligent coaching technology.

Chat with your game database! Ask questions about past matches, get insights, and receive personalized recommendations based on your complete playing history.

Analyze top-ranked players, upload match videos for detailed breakdowns, or simulate hypothetical matches. Learn from the best players in the world.

Upload videos of any sport and our AI automatically generates body tracking analysis. Perfect for coaches and athletes across multiple disciplines.

We're expanding our AI-powered coaching technology to bring you the same advanced analytics across multiple sports:

• ⚽ Football
• 🎾 Tennis
• ⚾ Baseball
• 🏏 Cricket
• 🏀 Basketball
• 🏐 Volleyball

• Frontend: HTML5, CSS3, JavaScript (ES6+)
• Icons: Font Awesome 6.4.0
• Authentication: Local Storage (Backend integration pending)
• Design: Responsive, Mobile-First Design
• Animations: CSS Animations & Transitions

1. Clone the repository:

   git clone https://github.com/Muhammadasharmian/PaddleCoach.git
   cd PaddleCoach

2. Open the project:

   • Simply open index.html in your web browser
   • Or use a local server:

     # Using Python
     python -m http.server 8000
     
     # Using Node.js (http-server)
     npx http-server

3. Access the application:

   • Open http://localhost:8000 in your browser

PaddleCoach/
├── index.html          # Main HTML file
├── styles.css          # All styling and responsive design
├── script.js           # JavaScript functionality and interactions
├── landing_image.png   # Hero section image
├── TASK_DIVISION.md    # Task distribution document
├── ping_pong_uml.md    # UML diagrams
└── README.md           # Project documentation

• User signup with email verification
• Login with persistent sessions (localStorage)
• User menu with logout functionality
• Protected feature access (login required)

• Sections reorder based on login state
• Hero section visibility control
• Personalized user experience

• Video upload for match analysis
• Support for multiple video formats
• File size and type validation

• Mobile-first approach
• Tablet and desktop optimizations
• Smooth animations and transitions

1. Sign Up: Users create an account with name, email, and password
2. Verification: Email verification step (UI implemented)
3. Login: Secure login with credential validation
4. Session: Persistent login state using localStorage
5. Protected Features: All feature buttons require authentication

1. Sign Up/Login to access all features
2. Upload Match Videos for AI analysis
3. Track Your Progress with ball and body tracking
4. Chat with AI Coach for personalized feedback
5. Compare with Pros to improve your technique

1. Multi-Sport Analysis for diverse training programs
2. Professional Analysis Tools for detailed breakdowns
3. Historical Data Access to track athlete progress

• Backend API integration
• Real video processing and analysis
• AI model integration for ball/body tracking
• WebRTC for live coaching sessions
• Payment gateway for Pro features
• Mobile app (iOS/Android)
• Multi-language support
• Social features (share, compete)

• Ashwani Mishra - @Ashwani564
• Mohnish Sao - @mohnish-dev
• Muhammad Ashar Mian - @Muhammadasharmian
• Rakshit Jaiswal - @Raksj12

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Project Link: https://github.com/Muhammadasharmian/PaddleCoach

• Font Awesome for icons
• AI/ML community for inspiration
• Table tennis community for valuable feedback