AI‐Assisted Self‐Powered Wearable Dual‐Mode Sensor With TENG and Stretchable Optical Fiber for Neurological Disorder Diagnostics

ABSTRACT Wearable sensors hold significant potential for managing lower‐limb dysfunction in neurological disorders, but current systems remain constrained by unimodal sensing, external power dependence, and limited diagnostic capabilities. Here, we present a wireless wearable dual‐mode sensor (WDMS) integrating three polyurethane‐based flexible optical strain (PFOS) components with a contact‐separation mode triboelectric nanogenerator (CS‐TENG). The PFOS components are used for muscle signal monitoring, while the CS‐TENG simultaneously monitors plantar pressure and harvests biomechanical energy to power the WDMS, eliminating external power dependence. Furthermore, leveraging gait data from 60 individuals, an embedded Convolutional Neural Network–Long Short‐Term Memory (CNN‐LSTM) model achieved 94.23% accuracy in distinguishing Parkinson's disease (PD) and stroke, while quantitatively evaluating rehabilitation progress after pharmacological and physical therapy interventions. By synergizing multimodal sensing, AI‐driven analysis, and clinical validation, this technology advances beyond passive monitoring to provide intelligent diagnostic support. Its self‐sufficiency and scalability facilitate transformative home‐based management of neurological disorders.