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Gesture Detection Using Teachable Machine

This project implements gesture detection using a model trained with Google's Teachable Machine. The application detects hand gestures (such as fist, palm, and directional movements) via webcam input and allows interaction with an on-screen element (a red square), which moves based on the detected gestures.

Features

  • Real-time gesture detection using a pre-trained model from Teachable Machine.
  • Webcam-based interface to detect hand gestures.
  • Moves an on-screen red square based on detected gestures:
    • Fist: Move right.
    • Palm: Move left.
    • Thumb up: Move up.
    • Thumb down: Move down.
  • Simple and intuitive UI with webcam feed display.

Technologies Used

  • Teachable Machine by Google
  • TensorFlow.js for pose estimation and gesture recognition
  • HTML5 Canvas for webcam feed and rendering keypoints/skeleton
  • JavaScript for real-time webcam control and DOM manipulation

How It Works

  1. The model is loaded from Teachable Machine and integrated using TensorFlow.js.
  2. The webcam is initialized, and the feed is displayed inside a canvas element.
  3. Hand gestures are detected in real-time, and the position of a red square on the screen is updated based on the detected gestures.

Installation

  1. Clone the repository: 
    git clone https://github.com/your-username/gesture-detection.git
  2. Open index.html in your preferred browser.

No additional installation steps are needed, as the project uses a CDN for TensorFlow.js and Teachable Machine.

Live Demo

View the live demo here

Usage

  • Open the app.
  • Ensure you allow access to the webcam when prompted.
  • Use hand gestures (fist, palm, thumb up, thumb down) to move the red square on the screen.

Future Enhancements

  • Add more gestures for additional functionality.
  • Improve gesture recognition accuracy.
  • Add sound or other feedback mechanisms for detected gestures.