QinwenZhong1 and Nahil Memon2, 1Huasiong College of Iloilo, Philippines, 2California State Polytechnic University, USA
ShuttleFit aims to improve access to affordable badminton training by providing AI-assisted biomechanical feedback through smartphone video analysis [1]. Traditional coaching is often costly, and ShuttleFit offers an alternative by integrating video upload and preprocessing, pose estimation and feedback, and progress tracking. Video handling is managed via Flask with cloud processing, while MediaPipe-based pose estimation and K-Means clustering enable movement analysis, though challenges exist in rapid motions and cluttered environments [2]. Initial experiments showed 64% accuracy, highlighting limitations in complex movement segmentation. Potential improvements include adopting DBSCAN for clustering and exploring edge computing to enhance performance. Unlike existing solutions requiring wearables or 3D motion capture, ShuttleFit’s smartphone-centric approach aims to lower costs and increase accessibility [3]. However, further research and development are required to fully validate its effectiveness, reliability, and real-world impact on badminton training.
AI-Assisted Coaching, Biomechanical Feedback, Pose Estimation, Badminton Training
