DensePhysioNet: AI-Driven Stress Detection Model using Physiological Dynamics

DensePhysioNet: AI-Driven Stress Detection Model using Physiological Dynamics | 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 DensePhysioNet: AI-Driven Stress Detection Model using Physiological Dynamics Manikandaprabhu Perumalsamy, Deepthi V, Deepa M B, Hari Prasad D, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8372851/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Mar, 2026 Read the published version in Journal on Advances in Signal Processing → Version 1 posted You are reading this latest preprint version Abstract Stress is a complex mind-body burden affecting health, life quality, and productivity, and underpins many physical and mental disorders. This paper describes an AI-based multimodal framework for stress detection from synchronized ECG, EMG, and BVP signals of the WESAD dataset that supports modelling cardiovascular, muscular, and vascular stress dynamics. After processing more than 60 million samples into 37,498 multimodal windows, the DensePhysioNet model achieves 98.0% accuracy, AUC 0.995, and an error rate of 0.0199, establishing its strong generalizability in effective multimodal learning. Stress predictions emerge 30 seconds in advance with interpretable physiological indicators and clinical-style alerts. Applications that could be developed based on this work include workplace mental health monitoring, remote patient assessment, high-risk occupational stress management, and next-generation wearables. The framework further supports Sustainable Development Goals (SDGs) in Good Health and Well-Being (SDG 3) by early detection and prevention of stress and reduction of long-term mental health burdens, and decent work and economic growth (SDG 8) through healthier and productive environments and reduction of burnout. Multimodal stress detection Physiological signals DensePhysioNet Early warning system Wearable sensors Cross-device fusion Good Health and Well-Being Decent Work and Economic Growth Sustainable Development Goals Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Mar, 2026 Read the published version in Journal on Advances in Signal Processing → Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8372851","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":564279842,"identity":"ea6b0e61-86ce-4667-acab-2f8a15358f1c","order_by":0,"name":"Manikandaprabhu 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