Deep Learning-Enabled Triboelectric Smart Insole for Gait Analysis
Human gait analysis consists of massive personal information which can be extracted via wearable sensors for human-machine interaction and security systems. Herein, a triboelectric smart insole is proposed for human gait analysis to achieve user activity recognition. The Siloxene/cobalt nanoporous carbon/P(VDF-TrFE) nanofiber was paired with Nylon 6/6 to generate the electric signals which were analyzed using cutting-edge deep learning technology. Furthermore, the smart insole consists of three triboelectric sensors to analyze the gait characteristics of the user, where an accuracy of 99% was achieved during user activity recognition, ensuring the high potential of the device for human gait analysis and security systems.
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