{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:58:28Z","timestamp":1760147908357,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T00:00:00Z","timestamp":1678665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Computer Science Department at Worcester Polytechnic Institute and the DARPA WASH","award":["HR00111780032-WASH-FP-031","FA8750-18-2-0077"],"award-info":[{"award-number":["HR00111780032-WASH-FP-031","FA8750-18-2-0077"]}]},{"DOI":"10.13039\/100000185","name":"DARPA","doi-asserted-by":"publisher","award":["HR00111780032-WASH-FP-031","FA8750-18-2-0077"],"award-info":[{"award-number":["HR00111780032-WASH-FP-031","FA8750-18-2-0077"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human context recognition (HCR) using sensor data is a crucial task in Context-Aware (CA) applications in domains such as healthcare and security. Supervised machine learning HCR models are trained using smartphone HCR datasets that are scripted or gathered in-the-wild. Scripted datasets are most accurate because of their consistent visit patterns. Supervised machine learning HCR models perform well on scripted datasets but poorly on realistic data. In-the-wild datasets are more realistic, but cause HCR models to perform worse due to data imbalance, missing or incorrect labels, and a wide variety of phone placements and device types. Lab-to-field approaches learn a robust data representation from a scripted, high-fidelity dataset, which is then used for enhancing performance on a noisy, in-the-wild dataset with similar labels. This research introduces Triplet-based Domain Adaptation for Context REcognition (Triple-DARE), a lab-to-field neural network method that combines three unique loss functions to enhance intra-class compactness and inter-class separation within the embedding space of multi-labeled datasets: (1) domain alignment loss in order to learn domain-invariant embeddings; (2) classification loss to preserve task-discriminative features; and (3) joint fusion triplet loss. Rigorous evaluations showed that Triple-DARE achieved 6.3% and 4.5% higher F1-score and classification, respectively, than state-of-the-art HCR baselines and outperformed non-adaptive HCR models by 44.6% and 10.7%, respectively.<\/jats:p>","DOI":"10.3390\/s23063081","type":"journal-article","created":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T03:04:46Z","timestamp":1678763086000},"page":"3081","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Domain Adaptation Methods for Lab-to-Field Human Context Recognition"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6725-4054","authenticated-orcid":false,"given":"Abdulaziz","family":"Alajaji","sequence":"first","affiliation":[{"name":"Data Science, Worcester Polytechnic Institute, Worcester, MA 01609, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Walter","family":"Gerych","sequence":"additional","affiliation":[{"name":"Data Science, Worcester Polytechnic Institute, Worcester, MA 01609, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luke","family":"Buquicchio","sequence":"additional","affiliation":[{"name":"Data Science, Worcester Polytechnic Institute, Worcester, MA 01609, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kavin","family":"Chandrasekaran","sequence":"additional","affiliation":[{"name":"Data Science, Worcester Polytechnic Institute, Worcester, MA 01609, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamid","family":"Mansoor","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Victoria, Victoria, BC V8P 5C2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3361-4952","authenticated-orcid":false,"given":"Emmanuel","family":"Agu","sequence":"additional","affiliation":[{"name":"Data Science, Worcester Polytechnic Institute, Worcester, MA 01609, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5375-9254","authenticated-orcid":false,"given":"Elke","family":"Rundensteiner","sequence":"additional","affiliation":[{"name":"Data Science, Worcester Polytechnic Institute, Worcester, MA 01609, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MPRV.2021.3051869","article-title":"Smartphone Health Biomarkers: Positive Unlabeled Learning of In-the-Wild Contexts","volume":"20","author":"Alajaji","year":"2021","journal-title":"IEEE Pervasive Comput."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., F\u00f6rster, K., Tr\u00f6ster, G., Lukowicz, P., Bannach, D., Pirkl, G., and Ferscha, A. 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