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Neural Eng."],"published-print":{"date-parts":[[2025,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective<\/jats:italic>. Human\u2013robot collaboration in lower-limb rehabilitation devices imposes stringent requirements on both the recognition accuracy of motion intention and real-time responsiveness. The precise recognition of lower limb motion based on surface electromyography (sEMG) has always been a primary focus of study. However, achieving low-delay recognition in lower limb continuous motion while maintaining accuracy remains a challenge, which is key to unlocking the full potential for the effective deployment and widespread application of robots. <jats:italic>Approach<\/jats:italic>. An innovative recognition method in lower limb continuous motion was presented in this paper, which investigated the instantaneous recognition network (IRN) and continuous recognition (CR) model. <jats:italic>Main results.<\/jats:italic> The comparative analysis revealed that by utilizing an optimal length of 210 for the onset-window sEMG data, the proposed IRN could substantially reduce the time delay from 300 ms\/350 ms to 60 ms at the methodological level. The implementation of the class-balanced method enhanced motion recognition accuracy by an additional 4.83% within the onset window. The CR model was validated across seven scenarios, comprehensively covering all potential situations in daily continuous movements, and achieved an average accuracy of 96.31%. <jats:italic>Significance.<\/jats:italic> This study demonstrates the potential of the proposed instantaneous recognition method to enhance performance in lower limb continuous motion, providing an innovative approach for research on human\u2013robot synchronization.<\/jats:p>","DOI":"10.1088\/1741-2552\/adfab3","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T22:49:19Z","timestamp":1755038959000},"page":"056003","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Instantaneous recognition method for lower limb continuous motion based on onset-window surface electromyography data"],"prefix":"10.1088","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0459-2931","authenticated-orcid":true,"given":"Xiaohui","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueyan","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0093-2480","authenticated-orcid":true,"given":"Xiaoyue","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dezhi","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2660-9697","authenticated-orcid":true,"given":"Wenzhuo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanglin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9111-2723","authenticated-orcid":true,"given":"Lin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2025,9,2]]},"reference":[{"key":"jneadfab3bib1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac1176","type":"journal-article","article-title":"Myoelectric control of robotic lower limb prostheses: a review of electromyography interfaces, control paradigms, challenges and future directions","volume":"18","author":"Fleming","year":"2021","journal-title":"J. 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