{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T05:21:18Z","timestamp":1788844878177,"version":"build-2803163510"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study aims to develop a assessment system for evaluating shoulder joint muscle strength in patients with varying degrees of upper limb injuries post-stroke, using surface electromyographic (sEMG) signals and joint motion data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The assessment system includes modules for acquiring muscle electromyography (EMG) signals and joint motion data. The EMG signals from the anterior, middle, and posterior deltoid muscles were collected, filtered, and denoised to extract time-domain features. Concurrently, shoulder joint motion data were captured using the MPU6050 sensor and processed for feature extraction. The extracted features from the sEMG and joint motion data were analyzed using three algorithms: Random Forest (RF), Backpropagation Neural Network (BPNN), and Support Vector Machines (SVM), to predict muscle strength through regression models. Model performance was evaluated using Root Mean Squared Error (<jats:italic>RMSE<\/jats:italic>), R-Square (<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>), Mean Absolute Error (<jats:italic>MAE<\/jats:italic>), and Mean Bias Error (<jats:italic>MBE<\/jats:italic>), to identify the most accurate regression prediction algorithm.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The system effectively collected and analyzed the sEMG from the deltoid muscles and shoulder joint motion data. Among the models tested, the Support Vector Regression (SVR) model achieved the highest accuracy with an <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> of 0.8059, <jats:italic>RMSE<\/jats:italic> of 0.2873, <jats:italic>MAE<\/jats:italic> of 0.2155, and <jats:italic>MBE<\/jats:italic> of 0.0071. The Random Forest model achieved an <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> of 0.7997, <jats:italic>RMSE<\/jats:italic> of 0.3039, <jats:italic>MAE<\/jats:italic> of 0.2405, and <jats:italic>MBE<\/jats:italic> of 0.0090. The BPNN model achieved an <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> of 0.7542, <jats:italic>RMSE<\/jats:italic> of 0.3173, <jats:italic>MAE<\/jats:italic> of 0.2306, and <jats:italic>MBE<\/jats:italic> of 0.0783.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The SVR model demonstrated superior accuracy in predicting muscle strength. The RF model, with its feature importance capabilities, provides valuable insights that can assist therapists in the muscle strength assessment process.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fneur.2024.1470759","type":"journal-article","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T17:17:25Z","timestamp":1734110245000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Design of upper limb muscle strength assessment system based on surface electromyography signals and joint motion"],"prefix":"10.3389","volume":"15","author":[{"given":"Siqi","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Lai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yipeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyu","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyue","family":"Gou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1016\/j.medj.2024.02.014","article-title":"Rehabilitation with brain-computer interface and upper limb motor function in ischemic stroke: a randomized controlled trial","volume":"5","author":"Wang","year":"2024","journal-title":"Medicine"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1259872","DOI":"10.3389\/fnins.2023.1259872","article-title":"Different doses of intermittent theta burst stimulation for upper limb motor dysfunction after stroke: a study protocol for a randomized controlled trial","volume":"17","author":"Tang","year":"2023","journal-title":"Front Neurosci"},{"key":"B3","doi-asserted-by":"publisher","first-page":"569","DOI":"10.3390\/healthcare12050569","article-title":"Efficacy of a rehabilitation program using mirror therapy and cognitive therapeutic exercise on upper limb functionality in patients with acute stroke","volume":"12","author":"Fern\u00e1ndez-Solana","year":"2024","journal-title":"Healthcare"},{"key":"B4","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1016\/j.rehab.2018.06.005","article-title":"Adding electrical stimulation during standard rehabilitation after stroke to improve motor function. 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