An Intelligent Badminton Handle With Multinode MEMS Sensors for Explainable Motion Recognition
Jian Li, Yibo Fan, Ruoyu Chen, Siyuan Liang, Yifei Feng, Ying He, Yuliang Zhao · IEEE Internet of Things Journal · 2025
Intelligent sensing technologies are transforming sports training by enabling precise motion analysis, critical for skill development and performance optimization. This study introduces a badminton racket handle embedded with a lightweight, multi-node MEMS-based sensing system designed for real-time motion recognition. To capture distributed grip forces, swing trajectories, and impact mechanics at the player-equipment interface, the system employs an ergonomic design ensuring natural gameplay. A hybrid feature extraction approach, integrating time-and frequency-domain features with a 1D-CNN, achieves a classification accuracy of 97.89% across ten badminton actions. To enhance interpretability and provide actionable insights, explainable AI using SMDL-attribution identifies key motion features, revealing biomechanical inefficiencies in grip strength, swing consistency, and wrist motion. Seamlessly integrated with Virtual Reality (VR) platforms, the system delivers immersive, real-time feedback, transforming training into an interactive and data-driven experience. By combining advanced sensing, machine learning, and explainable AI, this system establishes a new benchmark for intelligent sports monitoring, with broad applications in sports training, rehabilitation, and human-computer interaction.