Self-powered triboelectric-electromagnetic composite sensor based on Kresling structure for AIoT-assisted rehabilitation applications

Abstract

The rapid growth of the aging population and the rising prevalence of motor disorders demand intelligent, user-centric rehabilitation technologies. Integrating artificial intelligence and the Internet of Things (AIoT) into sensor devices offers a powerful means of capturing limb motion data and assisting rehabilitation, thereby helping patients regain confidence and functional independence. This work presents a self-powered sensor based on a Kresling-structured thermoplastic polyurethane (TPU) substrate that integrates triboelectric nanogenerators (TENGs) and electromagnetic generators (EMGs). Optimizing the Kresling geometry and stiffness of the Kresling structure achieves high adaptability to human motion and high-sensitivity monitoring. The bistable design enables synergistic TENG–EMG signal outputs under axial compression and circumferential torsion, leveraging TENG sensitivity and EMG stability for reliable low-frequency motion detection. Using machine learning framework extracts multi-scale motion features, enabling identity verification, limb activity monitoring, and precise wrist tracking with classification accuracy all above 98%. Based on composite sensor signals and human-machine interaction (HMI), immersive and assistive wrist rehabilitation training is achieved through real-time feedback and applications such as claw machine. Additionally, interactive platforms including a “Dancing Machine” and a “Driving Simulator” integrate the sensor to explore brain–body collaborative rehabilitation. This work provides a low-cost, energy-efficient, and scalable solution for next-generation intelligent rehabilitation, paving the way for personalized, immersive, and user-centric therapy systems.

Keywords

assisted rehabilitation / healthcare / human–machine interaction / Kresling structure / machine learning / triboelectric-electromagnetic sensor

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