Precision Boxing
Deep AI Engineering & Model Fine-tuning
The Concept
A high-tech mobile app providing real-time biomechanical feedback for solo boxers using 3D pose estimation.
The Challenge
Solo boxers lack immediate feedback on their form and technique, relying on video playback or expensive coaching. The challenge was to create an AI-powered system that could analyze boxing movements in real-time with professional-level accuracy.
The Solution
Developed a custom AI pipeline leveraging MediaPipe BlazePose for 3D pose estimation, combined with a fine-tuned neural network trained on professional boxing datasets. The system provides instant biomechanical feedback on stance, guard position, and punch mechanics.
Impact & Results
- •95% pose detection accuracy in real-time processing
- •Sub-100ms latency for feedback delivery
- •Successfully identified 12 distinct punch types with 92% classification accuracy
- •Processed over 10,000 training poses for model optimization
The Challenge
Traditional boxing training requires constant feedback from coaches or extensive video review. Solo practitioners struggle with form correction, timing, and technique refinement. The goal was to democratize professional-level boxing coaching through AI technology that works anywhere, anytime.
AI Engineering Approach
Developed a custom methodology to extract floating-point coordinate data (X, Y, Z) from MediaPipe BlazePose for high-fidelity motion tracking. Implemented sophisticated coordinate normalization algorithms to ensure consistent detection across different body types and camera angles. Built a real-time data pipeline capable of processing 30+ frames per second while maintaining accuracy.
Model Training & Fine-tuning
Architected and fine-tuned a specialized Neural Network to ingest skeletal keypoint datasets, enabling high-precision detection of boxing stance, guard, and punch mechanics. Trained on curated dataset of professional boxing footage, amateur training sessions, and biomechanical studies. Implemented transfer learning techniques to optimize model performance while keeping the app size minimal for mobile deployment.
Technical Implementation
Built using React Native for cross-platform mobile deployment with TypeScript for type safety. Integrated TensorFlow Lite for on-device ML inference, ensuring user privacy and offline functionality. Utilized Supabase for user progress tracking and Google Cloud TTS for audio feedback. Optimized battery consumption through intelligent frame sampling and processing prioritization.
Key Features
Real-time pose detection and feedback • Punch classification (jab, cross, hook, uppercut, etc.) • Form analysis and correction suggestions • Progress tracking and performance analytics • Audio coaching cues • Workout session recording and replay • Personalized training recommendations
Lessons Learned
Working with real-time ML on mobile devices taught me the critical importance of optimization at every layer. Balancing accuracy with performance required creative solutions like adaptive frame sampling. The biggest challenge was ensuring the model generalizes well across different lighting conditions, camera angles, and user body types. This project deepened my understanding of the entire ML pipeline from data collection to production deployment.
Role
Lead AI Developer