{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T09:40:15Z","timestamp":1775122815756,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,2,24]],"date-time":"2017-02-24T00:00:00Z","timestamp":1487894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61379067"],"award-info":[{"award-number":["61379067"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFB1001300"],"award-info":[{"award-number":["2016YFB1001300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>High-density surface electromyography (HD-sEMG) is to record muscles\u2019 electrical activity from a restricted area of the skin by using two dimensional arrays of closely spaced electrodes. This technique allows the analysis and modelling of sEMG signals in both the temporal and spatial domains, leading to new possibilities for studying next-generation muscle-computer interfaces (MCIs). sEMG-based gesture recognition has usually been investigated in an intra-session scenario, and the absence of a standard benchmark database limits the use of HD-sEMG in real-world MCI. To address these problems, we present a benchmark database of HD-sEMG recordings of hand gestures performed by 23 participants, based on an 8 \u00d7 16 electrode array, and propose a deep-learning-based domain adaptation framework to enhance sEMG-based inter-session gesture recognition. Experiments on NinaPro, CSL-HDEMG and our CapgMyo dataset validate that our approach outperforms state-of-the-arts methods on intra-session and effectively improved inter-session gesture recognition.<\/jats:p>","DOI":"10.3390\/s17030458","type":"journal-article","created":{"date-parts":[[2017,2,24]],"date-time":"2017-02-24T11:19:50Z","timestamp":1487935190000},"page":"458","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":312,"title":["Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0368-1115","authenticated-orcid":false,"given":"Yu","family":"Du","sequence":"first","affiliation":[{"name":"State Key Lab of CAD&amp;CG, College of Computer Science, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Wenguang","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Wentao","family":"Wei","sequence":"additional","affiliation":[{"name":"State Key Lab of CAD&amp;CG, College of Computer Science, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Yu","family":"Hu","sequence":"additional","affiliation":[{"name":"State Key Lab of CAD&amp;CG, College of Computer Science, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Weidong","family":"Geng","sequence":"additional","affiliation":[{"name":"State Key Lab of CAD&amp;CG, College of Computer Science, Zhejiang University, Hangzhou 310027, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Saponas, T.S., Tan, D.S., Morris, D., and Balakrishnan, R. 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