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However, there are few research studies on overcoming the influence of physiological factors among different individuals. In this paper, a cross-individual gesture recognition method based on long short-term memory (LSTM) networks is proposed, named cross-individual LSTM (CI-LSTM). CI-LSTM has a dual-network structure, including a gesture recognition module and an individual recognition module. By designing the loss function, the individual information recognition module assists the gesture recognition module to train, which tends to orthogonalize the gesture features and individual features to minimize the impact of individual information differences on gesture recognition. Through cross-individual gesture recognition experiments, it is verified that compared with other selected algorithm models, the recognition accuracy obtained by using the CI-LSTM model can be improved by an average of 9.15%. Compared with other models, CI-LSTM can overcome the influence of individual characteristics and complete the task of cross-individual hand gestures recognition. Based on the proposed model, online control of the prosthetic hand is realized.<\/jats:p>","DOI":"10.1155\/2021\/6680417","type":"journal-article","created":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T01:50:07Z","timestamp":1625795407000},"page":"1-11","source":"Crossref","is-referenced-by-count":5,"title":["Cross-Individual Gesture Recognition Based on Long Short-Term Memory Networks"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4845-0097","authenticated-orcid":true,"given":"Huasong","family":"Min","sequence":"first","affiliation":[{"name":"Laboratory for Embedded System and Intelligent Robot, Wuhan University of Science and Technology, Wuhan 430000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7991-984X","authenticated-orcid":true,"given":"Ziming","family":"Chen","sequence":"additional","affiliation":[{"name":"Laboratory for Embedded System and Intelligent Robot, Wuhan University of Science and Technology, Wuhan 430000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9149-7336","authenticated-orcid":true,"given":"Bin","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3798-0877","authenticated-orcid":true,"given":"Ziwei","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Engineering and Technology, China University of Geoscience (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixu","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongtao","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinghuangdao 066000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Special Heavy Load Robot, Anhui University of Technology, Ma\u2019anshan 243000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3546-6305","authenticated-orcid":true,"given":"Fuchun","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunfang","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Information and Technology, Beijing University of Technology, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1002\/cphy.c100087"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/acc.2008.4587146"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/cacs.2018.8606762"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899x\/121\/1\/012017"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aafc88"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/tbme.2013.2280900"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/10.914793"},{"key":"8","first-page":"1497","article-title":"EMG classification for prehensile postures using cascaded architecture of neural networks with self-organizing maps","author":"H. 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