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This study aims to classify and recognize nine types of upper limb motor intentions based on surface electromyography (sEMG) and apply them to the interactive control of an end-effector rehabilitation robot. The research begins with selecting muscles and data preprocessing, incorporating the generation mechanism of sEMG along with the anatomical and kinesiological principles of upper limb muscles. Next, a musculoskeletal model of the upper limb is established and validated through simulations in OpenSim. To avoid the drawbacks of modeling methods, traditional machine learning and deep learning methods are employed to perform a nine-class classification task on the sEMG data, comparing the classification accuracy of different approaches. Finally, the motor intentions extracted using a multi-stream convolutional neural network (MLCNN) are utilized to control the iReMo\u00ae end-effector rehabilitation robot, with the system\u2019s motion smoothness and accuracy evaluated through tests involving different trajectories.<\/jats:p>","DOI":"10.3390\/s25041057","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T05:34:32Z","timestamp":1739252072000},"page":"1057","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Research on Upper Limb Motion Intention Classification and Rehabilitation Robot Control Based on sEMG"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2319-1682","authenticated-orcid":false,"given":"Tao","family":"Song","sequence":"first","affiliation":[{"name":"Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China"},{"name":"Shanghai Golden Arrow Robot Technology Co., Ltd., 701, Building 3, No. 377 Shanlian Road, Baoshan District, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Yan","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuwen","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Guo","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China"},{"name":"National Demonstration Center for Experimental Engineering Training Education, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0524-2469","authenticated-orcid":false,"given":"Xianhua","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Anhui University of Science and Technology, Huainan 232001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4172\/2168-9695.1000e107","article-title":"EMG-based robot control interfaces: Past, present and future","volume":"1","author":"Artemiadis","year":"2012","journal-title":"Adv. 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