{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T18:57:34Z","timestamp":1774551454450,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T00:00:00Z","timestamp":1718928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Project, China","award":["2019YFE0110800"],"award-info":[{"award-number":["2019YFE0110800"]}]},{"name":"the National Key Research and Development Project, China","award":["62276040"],"award-info":[{"award-number":["62276040"]}]},{"name":"the National Key Research and Development Project, China","award":["62276041"],"award-info":[{"award-number":["62276041"]}]},{"name":"the National Key Research and Development Project, 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Technology Research Project, China","award":["2019YFE0110800"],"award-info":[{"award-number":["2019YFE0110800"]}]},{"name":"Chongqing Education Commission Science and Technology Research Project, China","award":["62276040"],"award-info":[{"award-number":["62276040"]}]},{"name":"Chongqing Education Commission Science and Technology Research Project, China","award":["62276041"],"award-info":[{"award-number":["62276041"]}]},{"name":"Chongqing Education Commission Science and Technology Research Project, China","award":["62221005"],"award-info":[{"award-number":["62221005"]}]},{"name":"Chongqing Education Commission Science and Technology Research Project, China","award":["61976031"],"award-info":[{"award-number":["61976031"]}]},{"name":"Chongqing Education Commission Science and Technology Research Project, China","award":["62027827"],"award-info":[{"award-number":["62027827"]}]},{"name":"Chongqing Education Commission Science and Technology Research Project, 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Funding","award":["62027827"],"award-info":[{"award-number":["62027827"]}]},{"name":"Chongqing Big Data Collaborative Innovation Center Funding","award":["KJQN202200624"],"award-info":[{"award-number":["KJQN202200624"]}]},{"name":"Chongqing Big Data Collaborative Innovation Center Funding","award":["CQBDCIC202303"],"award-info":[{"award-number":["CQBDCIC202303"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hand gesture recognition is pivotal in facilitating human\u2013machine interaction within the Internet of Things. Nevertheless, it encounters challenges, including labeling expenses and robustness. To tackle these issues, we propose a semi-supervised learning framework guided by pseudo-label consistency. This framework utilizes a dual-branch structure with a mean-teacher network. Within this setup, a global and locally guided self-supervised learning encoder acts as a feature extractor in a teacher\u2013student network to efficiently extract features, maximizing data utilization to enhance feature representation. Additionally, we introduce a pseudo-label Consistency-Guided Mean-Teacher model, where simulated noise is incorporated to generate newly unlabeled samples for the teacher model before advancing to the subsequent stage. By enforcing consistency constraints between the outputs of the teacher and student models, we alleviate accuracy degradation resulting from individual differences and interference from other body parts, thereby bolstering the network\u2019s robustness. Ultimately, the teacher model undergoes refinement through exponential moving averages to achieve stable weights. We evaluate our semi-supervised method on two publicly available hand gesture datasets and compare it with several state-of-the-art fully-supervised algorithms. The results demonstrate the robustness of our method, achieving an accuracy rate exceeding 99% across both datasets.<\/jats:p>","DOI":"10.3390\/rs16132267","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T08:50:08Z","timestamp":1718959808000},"page":"2267","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Semi-Supervised FMCW Radar Hand Gesture Recognition via Pseudo-Label Consistency Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Yuhang","family":"Shi","sequence":"first","affiliation":[{"name":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0138-2728","authenticated-orcid":false,"given":"Lihong","family":"Qiao","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"},{"name":"Chongqing Big Data Collaborative Innovation Center, Chongqing 401135, China"}]},{"given":"Yucheng","family":"Shu","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Baobin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, University of Chinese Academy of Sciences, Beijing 100871, China"}]},{"given":"Bin","family":"Xiao","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9033-8245","authenticated-orcid":false,"given":"Weisheng","family":"Li","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Xinbo","family":"Gao","sequence":"additional","affiliation":[{"name":"Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5008011","DOI":"10.1109\/TIM.2023.3246488","article-title":"Hand Gesture Recognition Using Densely Connected Deep Residual Network and Channel Attention Module for Mobile Robot Control","volume":"72","author":"Sahoo","year":"2023","journal-title":"IEEE Trans. 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