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To address these challenges, we present Human Heterogeneity Invariant Stress Sensing (HHISS), a domain generalization approach designed to find consistent patterns in stress signals by removing person-specific differences. This helps the model perform more accurately across new people, environments, and stress types not seen during training. Its novelty lies in proposing a novel technique called person-wise sub-network pruning intersection to focus on shared features across individuals, alongside preventing overfitting by leveraging continuous labels while training. The present study focuses on people with opioid use disorder (OUD)---a group where stress responses can change dramatically depending on the presents of opioids in their system, including daily timed medication for OUD (MOUD). Since stress often triggers cravings, a model that can adapt well to these changes could support better OUD rehabilitation and recovery. We tested HHISS on seven different stress datasets---four which we collected ourselves and three public datasets. Four are from lab setups, one from a controlled real-world driving setting, and two are from real-world in-the-wild field datasets with no constraints. The present study is the first known to evaluate how well a stress detection model works across such a wide range of data. Results show HHISS consistently outperformed state-of-the-art baseline methods, proving both effective and practical for real-world use. Ablation studies, empirical justifications, and runtime evaluations confirm HHISS's feasibility and scalability for mobile stress sensing in sensitive real-world applications.<\/jats:p>","DOI":"10.1145\/3749465","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T17:15:45Z","timestamp":1756919745000},"page":"1-42","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Human Heterogeneity Invariant Stress Sensing"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5261-5440","authenticated-orcid":false,"given":"Yi","family":"Xiao","sequence":"first","affiliation":[{"name":"Arizona State University, Tempe, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7016-6220","authenticated-orcid":false,"given":"Harshit","family":"Sharma","sequence":"additional","affiliation":[{"name":"Arizona State University, Tempe, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8937-3702","authenticated-orcid":false,"given":"Sawinder","family":"Kaur","sequence":"additional","affiliation":[{"name":"Syracuse University, Syracuse, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8852-732X","authenticated-orcid":false,"given":"Dessa","family":"Bergen-Cico","sequence":"additional","affiliation":[{"name":"Syracuse University, Syracuse, New York, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0807-8967","authenticated-orcid":false,"given":"Asif","family":"Salekin","sequence":"additional","affiliation":[{"name":"Arizona State University, Tempe, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Domain-adversarial neural networks. arXiv preprint arXiv:1412.4446","author":"Ajakan Hana","year":"2014","unstructured":"Hana Ajakan, Pascal Germain, Hugo Larochelle, Fran\u00e7ois Laviolette, and Mario Marchand. 2014. 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