Hybrid Electromagnetic-Triboelectric Hip Energy Harvester for Wearables and AI-Assisted Motion Monitoring

Reliable human motion monitoring is crucial across various fields such as sports, healthcare, and metaverse. This study introduces an AI-assisted wearable hip joint energy harvester (HJEH) designed to convert mechanical energy from hip joint movements into electric power for wearable devices while simultaneously monitoring human motion. The HJEH utilizes an electromagnetic generator (EMG) in conjunction with a freestanding triboelectric nanogenerator (FS-TENG) to achieve energy harvesting and motion sensing. The EMG specifically recovers the negative energy from hip joint motion to generate electricity, with a flywheel and acceleration gears employ to enhance output power. Concurrently, the FS-TENG generates triboelectric signals driven by hip joint motions, which are processed using deep learning algorithms for accurate motion detection. The performance of the HJEH is thoroughly evaluated through bench tests, treadmill tests, and outdoor experiments. The EMG achieves a peak power output of 357 mW and a maximum gravitational power density of 1.67 W kg⁻¹ during running at a speed of 8 km h⁻¹. The FS-TENG demonstrates a remarkable accuracy of 99.95% in identifying 12 different types of motion, validating the efficacy of the integrated motion monitoring system. In particular, incorporating digital twin technology realizes safety monitoring for the elderly.