{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:27:37Z","timestamp":1785544057716,"version":"3.56.0"},"reference-count":62,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T00:00:00Z","timestamp":1667952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Plan of China","award":["2021YFC2501202"],"award-info":[{"award-number":["2021YFC2501202"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61972383, 61902377, 62101530, 61902379"],"award-info":[{"award-number":["61972383, 61902377, 62101530, 61902379"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science and Technology Service Network Initiative, Chinese Academy of Sciences","award":["KFJ-STS-QYZD-2021-11-001"],"award-info":[{"award-number":["KFJ-STS-QYZD-2021-11-001"]}]},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association CAS","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2023,2,28]]},"abstract":"<jats:p>\n            Human activity recognition requires the efforts to build a generalizable model using the training datasets with the hope to achieve good performance in test datasets. However, in real applications, the training and testing datasets may have totally different distributions due to various reasons such as different body shapes, acting styles, and habits, damaging the model\u2019s generalization performance. While such a distribution gap can be reduced by existing domain adaptation approaches, they typically assume that the test data can be accessed in the training stage, which is not realistic. In this article, we consider a more practical and challenging scenario: domain-generalized activity recognition (DGAR) where the test dataset\n            <jats:italic>cannot<\/jats:italic>\n            be accessed during training. To this end, we propose\n            <jats:italic>Adaptive Feature Fusion for Activity Recognition\u00a0(AFFAR)<\/jats:italic>\n            , a domain generalization approach that learns to fuse the domain-invariant and domain-specific representations to improve the model\u2019s generalization performance. AFFAR takes the best of both worlds where domain-invariant representations enhance the transferability across domains and domain-specific representations leverage the model discrimination power from each domain. Extensive experiments on three public HAR datasets show its effectiveness. Furthermore, we apply AFFAR to a real application, i.e., the diagnosis of Children\u2019s Attention Deficit Hyperactivity Disorder\u00a0(ADHD), which also demonstrates the superiority of our approach.\n          <\/jats:p>","DOI":"10.1145\/3552434","type":"journal-article","created":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T11:28:46Z","timestamp":1659439726000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":47,"title":["Domain Generalization for Activity Recognition via Adaptive Feature Fusion"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4717-4310","authenticated-orcid":false,"given":"Xin","family":"Qin","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Mobile Computing and Pervasive Devices, Institute of Computing Technology, CAS, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4833-0880","authenticated-orcid":false,"given":"Jindong","family":"Wang","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8407-0780","authenticated-orcid":false,"given":"Yiqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Mobile Computing and Pervasive Devices, Institute of Computing Technology, CAS, University of Chinese Academy of Sciences, Pengcheng Laboratory, Shenzhen, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4035-0737","authenticated-orcid":false,"given":"Wang","family":"Lu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Mobile Computing and Pervasive Devices, Institute of Computing Technology, CAS, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6832-3808","authenticated-orcid":false,"given":"Xinlong","family":"Jiang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Mobile Computing and Pervasive Devices, Institute of Computing Technology, CAS, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,11,9]]},"reference":[{"key":"e_1_3_1_2_2","article-title":"Generalizing to unseen domains via distribution matching","author":"Albuquerque Isabela","year":"2019","unstructured":"Isabela Albuquerque, Jo\u00e3o Monteiro, Mohammad Darvishi, Tiago H. 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