Smart Furniture Using Radar Technology for Cardiac Health Monitoring

Smart Furniture Using Radar Technology for Cardiac Health Monitoring | 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 Article Smart Furniture Using Radar Technology for Cardiac Health Monitoring Ali Gharamohammadi, Mohammad Omid Bagheri, Serene Abu-Sardanah, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4189725/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The integration of radar technology into smart furniture represents a practical approach to health monitoring, circumventing the concerns regarding user convenience and privacy often encountered by conventional smart home systems. Radar technology's inherent non-contact methodology, privacy-preserving features, adaptability to diverse environmental conditions, and high precision characteristics collectively establish it a compelling alternative for comprehensive health monitoring within domestic environments. In this paper, we introduce a mm-wave radar system positioned strategically behind a seat, featuring an algorithm capable of identifying unique cardiac waveform patterns for healthy subjects. These patterns are characterized by two peaks followed by a valley in each cycle, which can be correlated to ECG, enabling effective cardiac waveform monitoring. The provided algorithm excels in discerning variations in heart patterns, particularly in individuals with prolonged corrected QT intervals, by minimizing high frequency breathing interference and ensuring accurate pattern recognition. Additionally, this paper addresses the influence of body movements in seated individuals, conducting a comprehensive study on heart rate variability and estimation. Experiment results demonstrate a maximum heart rate variability error of 30 milliseconds and an average relative error of 4.8% in heart rate estimation, showcasing the efficacy of the proposed method utilizing variational mode decomposition and a multi-bin approach. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Engineering/Biomedical engineering Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted 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. 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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-4189725","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":295066434,"identity":"590d69c1-621c-430e-b8b3-930f96290171","order_by":0,"name":"Ali Gharamohammadi","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-5304-5032","institution":"University of Waterloo","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"","lastName":"Gharamohammadi","suffix":""},{"id":295066435,"identity":"91e9e105-58ce-4e27-8213-388d011bcd8d","order_by":1,"name":"Mohammad Omid Bagheri","email":"","orcid":"https://orcid.org/0000-0002-5424-7848","institution":"University of Waterloo","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Omid","lastName":"Bagheri","suffix":""},{"id":295066436,"identity":"f2f32ce5-e2d8-4308-b2df-9187982571fb","order_by":2,"name":"Serene Abu-Sardanah","email":"","orcid":"","institution":"University of Waterloo","correspondingAuthor":false,"prefix":"","firstName":"Serene","middleName":"","lastName":"Abu-Sardanah","suffix":""},{"id":295066437,"identity":"f2c93e39-eb0e-46c1-9e6c-fee4d67fd795","order_by":3,"name":"Michael M. 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