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J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p> Cross-subject activity recognition is challenging in the human activity recognition field. Previous studies have often assumed that training and test data follow the same distribution, which is impractical in real-world applications. Thus, models\u2019 performance will significantly decline when applied to data collected from new unseen subjects because of the different physical conditions and human habits. To solve the above challenges, we proposed the regularized feature alignment (RFA) network. The RFA introduces a source domain selection mechanism (SDSM) based on calculating the Wasserstein distance between different subjects. Through SDSM, subjects with high similarity in the source domain can be retained, which implicitly compacts the feature subspace distribution. We implemented linear data augmentation on the retained subjects to mitigate the effects of the decline in the training set. In addition, the regularized dropout method was adopted to explicitly compact the feature subspace distributions. Finally, multi-level feature alignment is performed via maximum mean discrepancy regularization to precisely match the source and target domain. To demonstrate the effectiveness of RFA, comprehensive experiments were conducted on four public datasets under the iterative left-one-subject-out setting. The experimental results demonstrate that RFA outperformed the state-of-the-art methods in datasets with a large divergence between subjects and achieved performance comparable to the state-of-the-art methods in a subject-balanced dataset. <\/jats:p>","DOI":"10.1142\/s0218001425510085","type":"journal-article","created":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T13:54:56Z","timestamp":1744379696000},"source":"Crossref","is-referenced-by-count":0,"title":["RFA: Regularized Feature Alignment Method for Cross-Subject Human Activity Recognition"],"prefix":"10.1142","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7246-0012","authenticated-orcid":false,"given":"Zhe","family":"Yang","sequence":"first","affiliation":[{"name":"Computer Science and Technology, Zhejiang University of Technology, #288 Liuhe Road, Hangzhou, Zhejiang 310023, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5114-3963","authenticated-orcid":false,"given":"Hao","family":"Fu","sequence":"additional","affiliation":[{"name":"Information Science and Electronic Engineering, Zhejiang University, #38 Zheda Road, Hangzhou, Zhejiang 310027, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2555-343X","authenticated-orcid":false,"given":"Ruohong","family":"Huan","sequence":"additional","affiliation":[{"name":"Computer Science and Technology, Zhejiang University of Technology, #288 Liuhe Road, Hangzhou, Zhejiang 310023, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3060-1081","authenticated-orcid":false,"given":"Mengjie","family":"Qu","sequence":"additional","affiliation":[{"name":"Information Science and Electronic Engineering, Zhejiang University, #38 Zheda Road, Hangzhou, Zhejiang 310027, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9335-4291","authenticated-orcid":false,"given":"Yun","family":"Pan","sequence":"additional","affiliation":[{"name":"Information Science and Electronic Engineering, Zhejiang University, #38 Zheda Road, Hangzhou, Zhejiang 310027, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"key":"S0218001425510085BIB001","first-page":"437","volume-title":"European Symp. 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