{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T10:26:33Z","timestamp":1779359193144,"version":"3.51.4"},"reference-count":36,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T00:00:00Z","timestamp":1716508800000},"content-version":"vor","delay-in-days":23,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["202208420109"],"award-info":[{"award-number":["202208420109"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202346"],"award-info":[{"award-number":["62202346"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computer Animation &amp;amp; Virtual"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Human activity recognition (HAR) has significant potential in virtual sports applications. However, current HAR networks often prioritize high accuracy at the expense of practical application requirements, resulting in networks with large parameter counts and computational complexity. This can pose challenges for real\u2010time and efficient recognition. This paper proposes a hybrid lightweight DSANet network designed to address the challenges of real\u2010time performance and algorithmic complexity. The network utilizes a multi\u2010scale depthwise separable convolutional (Multi\u2010scale DWCNN) module to extract spatial information and a multi\u2010layer Gated Recurrent Unit (Multi\u2010layer GRU) module for temporal feature extraction. It also incorporates an improved channel\u2010space attention module called RCSFA to enhance feature extraction capability. By leveraging channel, spatial, and temporal information, the network achieves a low number of parameters with high accuracy. Experimental evaluations on UCIHAR, WISDM, and PAMAP2 datasets demonstrate that the network not only reduces parameter counts but also achieves accuracy rates of 97.55%, 98.99%, and 98.67%, respectively, compared to state\u2010of\u2010the\u2010art networks. This research provides valuable insights for the virtual sports field and presents a novel network for real\u2010time activity recognition deployment in embedded devices.<\/jats:p>","DOI":"10.1002\/cav.2274","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T08:38:35Z","timestamp":1716539915000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["DSANet: A lightweight hybrid network for human action recognition in virtual sports"],"prefix":"10.1002","volume":"35","author":[{"given":"Zhiyong","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Computer Science and Artificial Intelligence Wuhan Textile University  Wuhan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8252-5131","authenticated-orcid":false,"given":"Feng","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and 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