AI Coach for Badminton
Introduction
During an internship at Mitibase Technologies, I collaborated with Arpit Patil and Nancy Vachhani to develop an innovative AI model for coaching badminton players. The project aimed to revolutionize sports training through artificial intelligence.
Paper Link: IEEE Document
The Concept
The project, titled "AI Coach for Badminton", was designed to "create an AI model that could evaluate a badminton player's performance and provide feedback to help them improve their skills."
Key Findings
The AI model successfully identified performance errors such as:
- Inappropriate posture
- Incorrect racket handling
- Poor hip and leg movement
After players worked on these areas, the team observed significant improvements in:
- Total endurance
- Efficiency
- Performance
- Strength
Methodology
The comprehensive approach involved:
- Performance evaluation during gameplay
- Identifying technical flaws
- Analyzing player's nutrition, training, and sleep patterns
Tools and Technologies
- TrackNet: CNN for shuttlecock trajectory mapping
- OpenPose: Human skeleton key point detection
- YOLOv3: Player bounding box detection
- IMU and Opal Sensors: Motion and performance tracking
- Video Recording System: Multi-perspective player analysis
- Wireless Sensors: Racket speed and handle pressure measurement
Limitations and Future Directions
While the current study didn't cover all game analysis aspects, the team identified future research objectives:
- Real-time application of developed technologies
- Establishing benchmark values for player performance
- Developing comprehensive training models
- Expanding stroke analysis across player skill levels
The researchers aim to create a more advanced automated badminton skill evaluation system in future studies.