A self-powered knee motion recognition system based on hybrid energy harvesting

To achieve simultaneous motion monitoring and low-frequency energy harvesting, this study proposes a lightweight, compact, and high-precision wearable self-powered and self-sensing system (WPSS) that integrates energy harvesting with gait monitoring by utilizing the negative mechanical energy generated during knee joint motion. The system incorporates an electromagnetic generator (EMG), a sliding-rail triboelectric nanogenerator (SRT-TENG), and a magnetically excited piezoelectric nanogenerator (M-PENG). Inspired by a ratchet-pawl mechanism, the SRT-TENG enables angle detection with a resolution of 15° during knee extension. In addition, the preset tension of the spring induces periodic contact-separation interactions between the ratchet and pawl, generating structural coupling vibrations that excite the piezoelectric element to produce high-frequency electrical signals closely related to step frequency and motion speed. These coupling-induced signals serve as complementary gait features to the primary triboelectric and piezoelectric outputs, enabling enhanced multi-modal sensing without additional sensing units. The WPSS achieves a gait-recognition accuracy of up to 90% by processing the integrated multi-source signals through a convolutional neural network. Overall, the proposed system offers an efficient, accurate, and self-sustained solution for healthcare monitoring, rehabilitation, elderly care, and human-machine interaction, and provides a promising pathway for the advancement of wearable self-powered sensing technologies.