{"paper_id":"02c1f773-869c-43e7-88ce-955fb8119fca","body_text":"HAR-Adapt: Multi-Modal Semi-Supervised Domain Adaptation for Human Activity Recognition | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article HAR-Adapt: Multi-Modal Semi-Supervised Domain Adaptation for Human Activity Recognition Deniz NoorMohammadzadehMaleki, Mahdi Baghaei Oskouei, Amirfarhad Farhadi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9360353/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract This study presents a deep semi-supervised domain adaptation framework, termed Enhanced SWL-Adapt, designed to address key challenges in cross-user Human Activity Recognition (HAR), namely limited labeled data and significant inter-user distribution shifts. Unlike conventional methods that struggle with hardware noise and individual variability, the proposed approach employs a multi-modal input module that decomposes accelerometer, gyroscope, and magnetometer signals into separate streams and processes them through a dynamic sensor attention mechanism. A three-branch optimization strategy is adopted to learn discriminative and domain-invariant representations. In addition, a meta-learning-based sample weight allocator and a multi-level alignment mechanism are introduced to handle heterogeneous user shifts. Experimental results on the OPPORTUNITY dataset demonstrate that the proposed method consistently outperforms state-of-the-art approaches, achieving a 2.1% improvement in accuracy and a 1.87% gain in Macro-F1 score. Human Activity Recognition (HAR) Semi-Supervised Domain Adaptation Multi-Modal Sensor Fusion Cross-User Distribution Shift Contrastive Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 May, 2026 Reviewers agreed at journal 02 May, 2026 Reviews received at journal 29 Apr, 2026 Reviews received at journal 28 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 23 Apr, 2026 Editor assigned by journal 23 Apr, 2026 Editor invited by journal 22 Apr, 2026 Submission checks completed at journal 22 Apr, 2026 First submitted to journal 22 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-9360353\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":633602932,\"identity\":\"77559ea7-9233-4440-9277-b91db0ce3b63\",\"order_by\":0,\"name\":\"Deniz NoorMohammadzadehMaleki\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Islamic Azad University, 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