Real-Time Yoga Pose Correction System
AI-powered mobile app using MoveNet for skeleton keypoint detection and a custom CNN for 90%+ accurate pose classification.
01Overview
This project leverages on-device computer vision to democratize personal yoga instruction. It achieved over 90% accuracy across multiple poses, providing low-latency corrective feedback directly on the device.
02Features
- Real-time skeleton keypoint detection using MoveNet Lightning
- Custom CNN achieving 90%+ accuracy in pose classification
- Live corrective feedback system with visual overlays
- Cross-platform support via TensorFlow.js for web browsers
- Native Android implementation for low-latency performance
03Engineering Decisions
Chose MoveNet for its high performance on mobile hardware without requiring a cloud GPU.
Engineered the classification pipeline to be invariant to user distance and camera angle.
Integrated TensorFlow.js to enable a 'zero-install' web version alongside the native app.
04Challenges & Solutions
Challenge
Providing accurate feedback in varying lighting and environments.
Solution
Used robust keypoint normalization techniques to ensure the model focuses on joint relationships rather than absolute pixel positions.