An Intelligent Badminton Handle With Multinode MEMS Sensors for Explainable Motion Recognition

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, multinode 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 1-D-convolutional neural network (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.