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Herein, a multimodal sensor glove is developed for quantifying the severity of Parkinson's disease symptoms in patients\u2019 hands while assessing the hands\u2019 multifunctionality. Toward signal processing, various algorithms are used to quantify and analyze each signal: Exponentially Weighted Average algorithm and Kalman filter are used to filter out noise, normalization to process bending signals, K\u2010Means Cluster Analysis to classify muscle strength grades, and Back Propagation Neural Network to identify and classify tremor signals with an accuracy of 95.83%. Given the compelling features, the flexibility, muscle strength, and stability assessed by the glove and the clinical observations are proved to be highly consistent with Kappa values of 0.833, 0.867, and 0.937, respectively. The intraclass correlation coefficients obtained by reliability evaluation experiments for the three assessments are greater than 0.9, indicating that the system is reliable. The glove can be applied to assist in formulating targeted rehabilitation treatments and improve hand recovery efficiency.<\/jats:p>","DOI":"10.1002\/advs.202206982","type":"journal-article","created":{"date-parts":[[2023,5,7]],"date-time":"2023-05-07T23:44:23Z","timestamp":1683503063000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Learning Hand Kinematics for Parkinson's Disease Assessment Using a Multimodal Sensor Glove"],"prefix":"10.1002","volume":"10","author":[{"given":"Yu","family":"Li","sequence":"first","affiliation":[{"name":"School of Life Science and Technology Changchun University of Science and Technology  Changchun 130022 P. R. 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