A Fully Self‐Powered Digital Wearable System for the Auxiliary Treatment of Plantar Fasciitis

ABSTRACT Plantar fasciitis severely impairs daily life through persistent pain and limited mobility, whereas conventional treatments often lack real‐time monitoring and personalized feedback. This study introduces a fully self‐powered digital wearable system (FS‐DWS), integrating an arch support auxiliary (ASA) device, a wearable sensing system (WSS), and a machine learning‐driven closed‐loop visualized feedback system (VFS) to enable real‐time plantar pressure monitoring and abnormal gait recognition for the auxiliary treatment of plantar fasciitis. As a system‐level engineering achievement, the ASA module integrates elastic support with energy harvesting, alleviating plantar pressure and powering the wearable sensing system without any batteries, with a maximum power density of 41.6 mW/cm 3 , one order of magnitude higher than those of previously reported biomechanical energy harvesting devices. The VFS utilizes a flexible sensor array to collect dynamic pressure data, which is processed via a machine learning algorithm to achieve real‐time classification of 7 gait cycle phases with an accuracy of 99.3%, enabling identification of abnormal pressure distribution, causal tracing of gait deviations, and generation of personalized correction instructions. As a proof‐of‐concept study, the proposed dual‐function strategy of “physical support + intelligent regulation” provides an efficient and sustainable approach to the long‐term management of plantar fasciitis and supports a shift in therapeutic approach from passive relief to active correction.