{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T05:08:33Z","timestamp":1772082513084,"version":"3.50.1"},"reference-count":79,"publisher":"MJS Publishing, Medical Journals Sweden AB","license":[{"start":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T00:00:00Z","timestamp":1756339200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JRM"],"abstract":"<jats:p>Objective: The objective of this study was to investigate a more accurate and efficient technique for assessing spasticity in stroke patients via surface electromyography (sEMG).\nMethods: 45 hemiplegic individuals were recruited and spasticity was assessed via the modified Ashworth scale (MAS). Multichannel sEMG data were collected from 3 muscles: the long head of the biceps brachii (LB), the short head of the biceps brachii (SB), and the brachioradialis (BR). Both time-domain and frequency-domain features were extracted. A K-nearest neighbour (k-NN) classifier was used to develop a new feature vector consisting of multichannel sEMG features. Finally, a model using this new feature was constructed and evaluated for classification accuracy.\nResults: Data from 40 patients were analysed, revealing significant correlations between MAS scores and sEMG features. Specifically, MAS exhibited strong positive correlations with 3 time-domain features: root mean square (RMS), integral sEMG (iEMG), and envelope area (EA) (r\u2009&gt;\u20090.7). In contrast, frequency-domain features were negatively correlated with the MAS score (r\u2009&lt;\u2009\u20130.7). A single-channel model and a single-feature model were developed as baselines. A k-NN classifier using a novel feature vector \u2013 -integrating single-channel and single-feature data \u2013 enabled automatic spasticity grading, surpassing the performance of the baseline models. The proposed multichannel sEMG feature fusion model achieved an average accuracy of 78.7%, significantly outperforming both the single-channel model (LB: 66.0%, SB: 64.3%, BR: 70.4%) and the single-feature model (RMS 70.8%, iEMG 71.4%, and EA 63.4%).\nConclusions: Compared with single-channel and single-feature models, the k-NN model, which uses multichannel sEMG features, has superior accuracy in spasticity assessments and is a reliable tool for objective evaluation. This approach holds promise for enhancing rehabilitation strategies by enabling precise and data-driven efficacy assessments, ultimately improving patient outcomes.<\/jats:p>","DOI":"10.2340\/jrm.v57.43745","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T09:29:13Z","timestamp":1756373353000},"page":"jrm43745","source":"Crossref","is-referenced-by-count":1,"title":["Automated assessment of upper limb spasticity in stroke patients with fusion of multichannel surface electromyography features"],"prefix":"10.2340","volume":"57","author":[{"given":"Xin","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feiyu","family":"Nong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengliang","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaobin","family":"Long","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1183","published-online":{"date-parts":[[2025,8,28]]},"reference":[{"key":"124589","doi-asserted-by":"crossref","unstructured":"Gil-Garcia CA, Flores-Alvarez E, Cebrian-Garcia R, -Mendoza-Lopez AC, Gonzalez-Hermosillo LM, Garcia-Blanco MD, et al. 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