A Coaxial Triboelectric Fiber Sensor for Human Motion Recognition and Rehabilitation via Machine Learning

Abstract

This work presents the fabrication of a coaxial fiber triboelectric sensor (CFTES) designed for efficient energy harvesting and gesture detection in wearable electronics. The CFTES was fabricated using a facile one-step wet-spinning approach, with PVDF-HFP/CNTs/Carbon black as the conductive electrode and PVDF-HFP/MoS2 as the triboelectric layer. The incorporation of 1T phase MoS2 into the PVDF-HFP matrix significantly improves the sensor’s output owing to its electron capture capabilities. The sensor’s performance was carefully optimized by varying the weight percentage of MoS2, the thickness of the fiber core, and the CNT ratio. The optimized CFTES, with a core thickness of 156 µm and 0.6 wt% MoS2, achieved a stable output voltage of ~8.2 V at a frequency of 4 Hz and 10 N applied force, exhibiting remarkable robustness over 3600 s. Furthermore, the CFTES effectively detects human finger gestures, with machine learning algorithms further enhancing its accuracy. This innovative sensor offers a sustainable solution for energy transformation and has promising applications in smart portable power sources and wearable electronic devices.