{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:55:56Z","timestamp":1783526156457,"version":"3.55.0"},"reference-count":31,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:00:00Z","timestamp":1743638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Mechanomyography (MMG) is a non-invasive technique for assessing muscle activity by measuring mechanical signals, offering high sensitivity and real-time monitoring capabilities, and it has many applications in rehabilitation training. Traditional MMG-based motion recognition relies on feature extraction and classifier training, which require segmenting continuous actions, leading to challenges in real-time performance and segmentation accuracy. Therefore, this paper proposes an innovative method for the real-time segmentation and classification of upper limb rehabilitation actions based on the You Only Look Once (YOLO) algorithm, integrating the Squeeze-and-Excitation (SE) attention mechanism to enhance the model\u2019s performance. In this paper, the collected MMG signals were transformed into one-dimensional time-series images. After image processing, the training set and test set were divided for the training and testing of the YOLOv5s-SE model. The results demonstrated that the proposed model effectively segmented isolated and continuous MMG motions while simultaneously performing real-time motion category prediction and outputting results. In segmentation tasks, the base YOLOv5s model achieved 97.9% precision and 98.0% recall, while the improved YOLOv5s-SE model increased precision to 98.7% (+0.8%) and recall to 98.3% (+0.3%). Additionally, the model demonstrated exceptional accuracy in predicting motion categories, achieving an accuracy of 98.9%. This method realizes the automatic segmentation of time-domain motions, avoids the limitations of manual parameter adjustment in traditional methods, and simultaneously enhances the real-time performance of MMG motion recognition through image processing, providing an effective solution for motion analysis in wearable devices.<\/jats:p>","DOI":"10.3390\/s25072257","type":"journal-article","created":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T05:51:26Z","timestamp":1743659486000},"page":"2257","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MMG-Based Motion Segmentation and Recognition of Upper Limb Rehabilitation Using the YOLOv5s-SE"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7937-4977","authenticated-orcid":false,"given":"Gangsheng","family":"Cao","sequence":"first","affiliation":[{"name":"School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shen","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunming","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China"},{"name":"School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13019","DOI":"10.1109\/JSEN.2021.3068521","article-title":"Advances and Disturbances in sEMG-Based Intentions and Movements Recognition: A Review","volume":"21","author":"Xu","year":"2021","journal-title":"IEEE Sens. 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