Motion Energy Management Optimization and Health Monitoring Based on TENG Wearable Terminal

Abstract

Wearable health monitoring technology is rapidly developing and showing great potential in sports rehabilitation, chronic disease management, and personalized medicine. However, existing methods generally rely on traditional batteries, which have problems such as insufficient energy supply, poor monitoring continuity, and insufficient accuracy in identifying complex health states. Therefore, the research proposes a motion energy management optimization and health monitoring method based on Triboelectric Nanogenerator (TENG) wearable terminals, which achieves efficient acquisition, stable conversion, and adaptive scheduling of low-frequency human motion energy through a composite TENG structure and power management circuit. It also combines multi-source physiological signal preprocessing and deep learning models to enhance health status recognition capabilities. In experimental testing, the average output power of the composite TENG at 3.0Hz reached 1057.20μW, with an end-to-end efficiency of up to 88.90%, significantly better than that of the control circuit. The signal preprocessing module improved the signal-to-noise ratio to 15.47dB and the classification accuracy to 0.937. The deep learning model had a recognition accuracy of 0.936, a robustness index of only 5.80%, and could operate stably for 18.60h under energy limited conditions, with an average power consumption of only 4.80mW. This proves that the proposed method can achieve low-power, long-term, and high reliability closed-loop integrated health monitoring, providing new ideas for sustainable wearable systems.