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[Submitted on 11 Aug 2026] Title:On-Device Language Models for Privacy-Preserving Stress Prediction: A Multimodal Evaluation on Mobile Health View a PDF of the paper titled On-Device Language Models for Privacy-Preserving Stress Prediction: A Multimodal Evaluation on Mobile Health, by Ibukunoluwa Soyebo and 4 other authors View PDF HTML (experimental) Abstract:Stress is a pervasive determinant of mental health and a key target for mobile health interventions. On-device language models (ODLMs) offer privacy-preserving inference without cloud dependency, yet their feasibility for health prediction under mobile resource constraints remains underexplored. We evaluate ODLMs for multi-modal stress prediction using zero-shot prompting, measuring predictive accuracy alongside latency and throughput. Our results show that objective sensor features marginally outperform subjective self-reports on average, and that lightweight sub-2B models achieve low latency with predictable resource usage. Our findings highlight both the promise and the practical constraints of ODLMs for mobile mental health. Comments: 6 pages, 2 figures. Received Honorable Mention at the 2026 Human-centered AI Research for Mental health, an Open Networking Symposium (HARMONY 2026) workshop, co-located with IEEE/ACM Conference on Connected Health: Applications, Systems, and Engineering Technologies (CHASE 2026) Subjects: Machine Learning (cs.LG); Human-Computer Interaction (cs.HC) Cite as: arXiv:2609.11961 [cs.LG] (or arXiv:2609.11961v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.11961 arXiv-issued DOI via DataCite Submission history From: Alyssa Donawa [view email] [v1] Tue, 11 Aug 2026 07:23:22 UTC (216 KB) Full-text links: Access Paper: View a PDF of the paper titled On-Device Language Models for Privacy-Preserving Stress Prediction: A Multimodal Evaluation on Mobile Health, by Ibukunoluwa Soyebo and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG new | recent | 2026-09 Change to browse by: cs cs.HC References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)