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However, existing frameworks designed for stable environments encounter challenges when faced with changing circumstances and imbalanced training datasets in realistic scenarios. In this article, we address both issues from a unified perspective by exploring a more generalized local minima. Initially, we revisit existing solutions and empirically observe the presence of sharp minima in trained long-tailed WiFi-based HAR models. Consequently, we propose a novel method called Class Region Flattening (\n            <jats:monospace>CRF<\/jats:monospace>\n            ) to identify class-conditional flat minima. This approach effectively mitigates bias caused by the long-tailed distribution and enhances generalization capabilities in the face of changing circumstances. Furthermore, we introduce a selective flattening operation to prevent optimization conflicts among different activity categories and reduce computational overhead. We integrate\n            <jats:monospace>CRF<\/jats:monospace>\n            into mainstream WiFi-based HAR models and evaluate their performance using our collected WiFi-based HAR dataset. Through extensive experiments, we demonstrate that the incorporation of\n            <jats:monospace>CRF<\/jats:monospace>\n            leads to significant improvements in performance. These findings underscore the effectiveness of\n            <jats:monospace>CRF<\/jats:monospace>\n            in addressing the challenges posed by changing circumstances and imbalanced training datasets in WiFi-based HAR.\n          <\/jats:p>\n          <jats:p\/>","DOI":"10.1145\/3757321","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T11:22:13Z","timestamp":1754479333000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Towards Stable WiFi-based HAR from Imbalanced Data and Changing Circumstances"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2886-818X","authenticated-orcid":false,"given":"Youquan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China","place":["Hefei, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1564-5800","authenticated-orcid":false,"given":"Zhipeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China","place":["Hefei, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3609-2205","authenticated-orcid":false,"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[{"name":"Southeast University","place":["Nanjing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2641-1708","authenticated-orcid":false,"given":"Xianjun","family":"Deng","sequence":"additional","affiliation":[{"name":"Cyber Science and Engineering, Huazhong University of Science and Technology","place":["Wuhan, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9348-2982","authenticated-orcid":false,"given":"Wei","family":"Xi","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong University","place":["Xi'an, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2986-3956","authenticated-orcid":false,"given":"Wei","family":"Gong","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China","place":["Hefei, China"]}]}],"member":"320","published-online":{"date-parts":[[2025,9,24]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Hana Ajakan Pascal Germain Hugo Larochelle Fran\u00e7ois Laviolette and Mario Marchand. 2014. 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