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However, most existing DG-based works have merely focused on modeling statistical dependence between sensor data and activity labels, neglecting the importance of intrinsic casual mechanism. Intuitively, every sensor input can be viewed as a mixture of causal (category-aware) and non-causal factors (domain-specific), where only the former affects activity classification judgment. In this paper, by casting such DG-based HAR as a casual inference problem, we propose a causality-inspired representation learning algorithm for cross-domain activity recognition. To this end, an early-forking two-branch framework is designed, where two separate branches are respectively responsible for learning casual and non-causal features, while an independence-based Hilbert-Schmidt Information Criterion is employed to implicitly disentangling them. Additionally, an inhomogeneous domain sampling strategy is designed to enhance disentanglement, while a category-aware domain perturbation layer is performed to prevent representation collapse. Extensive experiments on several public HAR benchmarks demonstrate that our causality-inspired approach significantly outperforms eleven related state-of-the-art baselines under cross-person, cross-dataset, and cross-position settings. Detailed ablation and visualizations analyses reveal underlying casual mechanism, indicating its effectiveness, efficiency, and universality in cross-domain activity recognition scenario.<\/jats:p>","DOI":"10.1145\/3729495","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T21:21:56Z","timestamp":1750281716000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deconfounding Causal Inference through Two-branch Framework with Early-forking for Sensor-based Cross-domain Activity Recognition"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6445-7863","authenticated-orcid":false,"given":"Di","family":"Xiong","sequence":"first","affiliation":[{"name":"School of Electrical and Automation Engineering, Nanjing Normal University, Jiang Su, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8749-7459","authenticated-orcid":false,"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical and Automation Engineering, Nanjing Normal University, Naning, Jiang Su, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1795-4161","authenticated-orcid":false,"given":"Shuoyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, Guang Dong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1575-6292","authenticated-orcid":false,"given":"Dongzhou","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Southeast University, Naning, Jiang Su, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6664-1172","authenticated-orcid":false,"given":"Wenbo","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Naning, Jiang Su, China"}]}],"member":"320","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2010.04.019"},{"key":"e_1_2_1_2_1","first-page":"3","article-title":"A public domain dataset for human activity recognition using smartphones","volume":"3","author":"Anguita Davide","year":"2013","unstructured":"Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge Luis Reyes-Ortiz, et al. 2013. 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