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To address these challenges, this paper proposes a novel gait analysis approach that integrates 3D joint motion estimation, spatiotemporal feature modeling and attention-based feature selection. Specifically, we first utilize a 3D pose estimation network to map 2D key points to 3D space, generating comprehensive skeleton information across spatial and temporal dimensions. Next, spatiotemporal graph convolutional networks (ST-GCN) are employed to model the skeleton sequence, enabling the extraction of dynamic gait features. Finally, a multi-head attention mechanism is introduced to refine feature selection and enhance recognition accuracy. Experimental results on datasets such as OUMVLP and GREW demonstrate that the proposed method outperforms existing approaches across all metrics, achieving a remarkable average precision of 92.4% on the OUMVLP dataset. Additionally, ablation studies confirm the significant contributions of each module to the overall performance improvement in gait recognition. <\/jats:p>","DOI":"10.1142\/s0218126625503086","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T08:05:13Z","timestamp":1743149113000},"source":"Crossref","is-referenced-by-count":0,"title":["A Gait Recognition Method Using 3D Joint Motion Estimation Spatiotemporal Modeling and Attention Mechanism"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3677-1129","authenticated-orcid":false,"given":"Rusong","family":"Wei","sequence":"first","affiliation":[{"name":"School of Cultural Tourism and Management, Guangxi Technological College of Machinery and Electricity, Nanning 530003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7298-839X","authenticated-orcid":false,"given":"Feng","family":"Lin","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence Technology, Guangxi Technological College of Machinery and Electricity, Nanning 530003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8237-1599","authenticated-orcid":false,"given":"Weijie","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Physical Education, Guangxi University, Nanning 530003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2025,6,7]]},"reference":[{"key":"S0218126625503086BIB001","doi-asserted-by":"publisher","DOI":"10.1109\/TGCN.2024.3379960"},{"key":"S0218126625503086BIB002","doi-asserted-by":"publisher","DOI":"10.1155\/2024\/5582660"},{"key":"S0218126625503086BIB003","first-page":"487","volume-title":"Computer Vision: A Reference Guide","author":"Makihara Y.","year":"2020"},{"key":"S0218126625503086BIB004","doi-asserted-by":"publisher","DOI":"10.1145\/3230633"},{"key":"S0218126625503086BIB005","doi-asserted-by":"publisher","DOI":"10.1016\/j.gaitpost.2023.10.019"},{"key":"S0218126625503086BIB006","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2024.3506473"},{"key":"S0218126625503086BIB007","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3447046"},{"key":"S0218126625503086BIB008","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2024.07.066"},{"key":"S0218126625503086BIB009","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3151865"},{"key":"S0218126625503086BIB010","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2879896"},{"key":"S0218126625503086BIB011","doi-asserted-by":"publisher","DOI":"10.3390\/s140508895"},{"key":"S0218126625503086BIB012","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-012-9341-3"},{"key":"S0218126625503086BIB013","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-5225-0193-0.ch007"},{"key":"S0218126625503086BIB014","doi-asserted-by":"publisher","DOI":"10.1016\/j.gaitpost.2017.06.019"},{"key":"S0218126625503086BIB015","doi-asserted-by":"publisher","DOI":"10.1016\/j.joca.2016.03.008"},{"key":"S0218126625503086BIB016","first-page":"e57063","volume":"133","author":"Mukaino M.","year":"2018","journal-title":"J. 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