Smart wristband for instrument and athletic training via triboelectric sensing and machine learning

Artificial intelligence (AI) has been widely adopted in augmented training systems for instrumental music performance and athletic skill development. However, current systems primarily rely on computer vision-based motion tracking, which faces inherent limitations such as restricted accuracy, operational complexity, susceptibility to environmental interference, and privacy concerns. Additionally, these systems often fail to detect subtle performance deviations invisible to visual sensors. To address these challenges, we developed a smart wristband that leverages the triboelectric effect to monitor the visible and invisible motion during sports and musical instrument practice. Integrated with machine learning algorithms, the wristband accurately distinguishes correct from incorrect drumming motions and recognizes various badminton hand gestures, achieving an accuracy of 98.3%. Moreover, the device employs low-cost materials of silicone and fluorinated ethylene propylene, ensuring affordability and scalability. By providing reliable motion analysis without the constraints of camera-based systems, this technology offers a robust and practical solution for next-generation training assistance.