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Here, we present a wireless wearable dual\u2010mode sensor (WDMS) integrating three polyurethane\u2010based flexible optical strain (PFOS) components with a contact\u2010separation mode triboelectric nanogenerator (CS\u2010TENG). The PFOS components are used for muscle signal monitoring, while the CS\u2010TENG 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\u2013Long Short\u2010Term Memory (CNN\u2010LSTM) 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\u2010driven analysis, and clinical validation, this technology advances beyond passive monitoring to provide intelligent diagnostic support. Its self\u2010sufficiency and scalability facilitate transformative home\u2010based management of neurological disorders.<\/jats:p>","DOI":"10.1002\/advs.202522179","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T11:08:10Z","timestamp":1773313690000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["AI\u2010Assisted Self\u2010Powered Wearable Dual\u2010Mode Sensor With TENG and Stretchable Optical Fiber for Neurological Disorder Diagnostics"],"prefix":"10.1002","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7323-169X","authenticated-orcid":false,"given":"Tianliang","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechanical and Electronic Engineering Wuhan University of Technology  Wuhan Hubei China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electronic Engineering Wuhan University of Technology  Wuhan Hubei China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoxu","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of High\u2010Entropy Energy Materials and Devices Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences  Beijing P. 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