{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T15:10:41Z","timestamp":1744902641380},"reference-count":33,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Human. Robot."],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p> Electromyography (EMG) has been widely accepted to interact with prosthetic hands, but still limited to using few channels for the control of few degrees of freedom. The use of more channels can improve the controllability, but it also increases system\u2019s complexity and reduces its wearability. It is yet clear if optimizely placing the EMG channel could provide a feasible solution to this challenge. This study customized a genetic algorithm to optimize the number of channels and its position on the forearm in inter-day hand gesture recognition scenario. Our experimental results demonstrate that optimally selected 14 channels out of 16 can reach a peak inter-day hand gesture recognition accuracy at 72.3%, and optimally selecting 9 and 11 channels would reduce the performance by 3% and 10%. The cross-validation results also demonstrate that the optimally selected EMG channels from five subjects also work on the rest of the subjects, improving the accuracies by 3.09% and 4.5% in 9- and 11-channel combination, respectively. In sum, this study demonstrates the feasibility of channel reduction through genetic algorithm, and preliminary proves the significance of EMG channel optimization for human\u2013machine interaction. <\/jats:p>","DOI":"10.1142\/s0219843619410019","type":"journal-article","created":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T02:21:13Z","timestamp":1557800473000},"page":"1941001","source":"Crossref","is-referenced-by-count":9,"title":["Facilitate sEMG-Based Human\u2013Machine Interaction Through Channel Optimization"],"prefix":"10.1142","volume":"16","author":[{"given":"Zheng","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science &amp; Technology, Zhejiang University of Technology, 288 Liuhe Rd, Hangzhou 310023, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinfeng","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Communication Engineering, Hangzhou Dianzi University, 1158, No. 2 Avenue, Xiasha, Hangzhou 310018, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gongfa","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Institute of Precision Manufacturing, 947 Heping Avenue, Wuhan 430081, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Honghai","family":"Liu","sequence":"additional","affiliation":[{"name":"Intelligent Systems and Biomedical Robotics Group, School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK"},{"name":"School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P. R. 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