This project explores the use of computer vision and mobile AI to analyze boxing technique and movement patterns in real time.
The platform is designed to support technical training, injury prevention, and performance awareness by providing structured, data-driven feedback directly from a mobile device.
The focus is not entertainment or gamification, but applied AI in a real-world training context, particularly for communities with limited access to professional coaching or sports science tools.
Boxing practitioners in urban training environments often face:
- Lack of structured technical progression
- High injury rates caused by incorrect posture and repetition
- No reliable metrics to measure improvement over time
Most existing solutions are either informal or inaccessible, leaving a gap between sports science and everyday training.
This application applies computer vision techniques to:
- Detect body posture and movement patterns
- Analyze strike execution and alignment
- Provide actionable feedback for safer and more effective training
The system is optimized for mobile environments, prioritizing performance, responsiveness, and usability in real training scenarios.
- Real-time pose estimation and motion tracking
- Mobile-optimized AI inference
- Scalable architecture for future training metrics and analytics
- Separation of research logic and application layers
The project is framed as an applied research initiative with potential impact in:
- Injury prevention
- Training retention
- Community health metrics
It serves as a foundation for pilot studies, public health partnerships, and further AI-driven sports research.
This project is under active development and experimentation.