As an emerging self‐powered sensing technology, the triboelectric nanogenerator (TENG) has become a research focus in the field of gait monitoring due to its high energy conversion efficiency and high sensitivity. Leveraging the coupled mechanisms of triboelectrification and electrostatic induction, TENG enables simultaneous harvesting of human motion energy and multidimensional motion signal perception, providing an innovative solution for liberating wearable devices from dependence on traditional power sources and achieving long‐term dynamic monitoring. This study systemay tically reviews the key advancements and application potential of TENG in gait monitoring. First, material innovations have significantly improved energy conversion efficiency and environmental adaptability. Second, structural optimization has achieved high mechanical response sensitivity and wearing comfort. Finally, the synergistic application of system integration and machine learning algorithms has broken through the bottleneck of complex motion signal analysis, while also highlighting the critical trade‐offs between model complexity, overfitting risks, and cross‐user generalizability. Future research needs to focus on the collaborative optimization of materials, algorithms, thereby accelerating the deep application of TENG in scenarios such as medical rehabilitation, sports science, and human–computer interaction. This paper provides a theoretical reference and technical outlook for the further development of TENG technology in the field of intelligent health monitoring.
Zhang et al. (Thu,) studied this question.
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