EdgeCaps: Pruned Capsule Networks for RF Vital Sign Measurement in Real-time

EdgeCaps: Pruned Capsule Networks for RF Vital Sign Measurement in Real-time | 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 EdgeCaps: Pruned Capsule Networks for RF Vital Sign Measurement in Real-time Qammer Abbasi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6941397/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Radio frequency (RF) sensing enables both non-contact and non-invasive monitoring of vital signs, offering a promising solution for unobtrusive health assessment. Recent advances in machine learning (ML) and deep learning (DL) have significantly improved the accuracy and reliability of RF-based vital sign monitoring. However, despite their high performance, state-of-the-art DL models are often computationally intensive and energy-demanding, making them unsuitable for continuous use on low-cost, resource-constrained edge devices. Moreover, real-time deployment of such models on embedded platforms remains underexplored in the literature. To address this challenge, we propose \textit{EdgeCaps}, a system that compresses a high-capacity capsule neural network (CapsNet) into a compact student model optimized for edge deployment. Specifically, EdgeCaps distills the knowledge of a 33.7 million-parameter teacher model into a compact student model with only 2.11 million parameters, while maintaining comparable representational capability. Using knowledge distillation (KD)-where a smaller model (student) learns to replicate the outputs of a larger model (teacher)-the student model achieves an accuracy of 85.5%, just two percentage points below the teacher, despite a 94% reduction in parameter count. We validate the proposed system through continuous respiration monitoring of five volunteers using a Raspberry Pi 4. A Monte Carlo-based evaluation strategy is employed to measure the generalization and robustness of the results. During deployment, the student model maintains a mean inference latency below 0.3 s, RAM usage around 18 %, and core temperature consistently under 44 °C, all within safe operational limits. In contrast, the teacher model exhibits latencies ranging from 0.4 s to 0.5 s, core temperatures up to 51 °C, and RAM usage up to 22%. These results demonstrate that EdgeCaps enables real-time, energy-efficient, and thermally stable RF-based vital sign monitoring on edge devices without reliance on off-device computation. To our knowledge, this work is the first to introduce a KD-based CapsNet for RF sensing and the first to provide a comprehensive end-to-end evaluation of continuous physiological monitoring on embedded hardware. Physical sciences/Engineering/Biomedical engineering Physical sciences/Engineering/Electrical and electronic engineering Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review 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. 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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-6941397","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":484967452,"identity":"bc887f8f-bf19-4038-bd59-8e51c6f9858a","order_by":0,"name":"Qammer Abbasi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABK0lEQVRIiWNgGAWjYDADAwkgkVDBIIMkxkaMljMMPCRqYWwjQgvf8dOpGz7uYZA3l25+/OHhvFoe/v7jFx/zMNjJM0ikJWDTInkmd9vNGc8YDHfOOWYmkbjtOI/EjZxiYx6GZMMGibQDWN1zIHfbbZ4DDIwbbiSYMSRuO8bDcIMnTTqHgTmBQSK9AauW82+33f5zgMF+w430zx8S5xzjkT9/BqSlHreWG0BbGA4wJG64kWMgkdhQw2NwIP0YUMthoBbsDpO88XbbzZ4DEsk755wpk0g4doDH8EYOs/Efg+OGbTzPsHqf73zuths/DtjYbpdu3/zxR02dnNz54w8fzqioludnTzPApoUBYrcEjHsYiHmAKg3wRCSac+uAmP0BLsWjYBSMglEwMgEAZTZpcr54cMIAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-7097-9969","institution":"University of Glasgow","correspondingAuthor":true,"prefix":"","firstName":"Qammer","middleName":"","lastName":"Abbasi","suffix":""}],"badges":[],"createdAt":"2025-06-20 20:15:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6941397/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6941397/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89390373,"identity":"ae9b8687-e892-4dec-8314-c50844cb92dc","added_by":"auto","created_at":"2025-08-19 12:53:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11641324,"visible":true,"origin":"","legend":"Article File","description":"","filename":"EdgeCapsPrunedCapsuleNetworksforRFVitalSignMeasurementinRealTime.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6941397/v1_covered_b6dcacde-9f75-4aa0-85fb-a20329b53f6b.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"EdgeCaps: Pruned Capsule Networks for RF Vital Sign Measurement in Real-time","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6941397/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6941397/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Radio frequency (RF) sensing enables both non-contact and non-invasive monitoring of vital signs, offering a promising solution for unobtrusive health assessment. Recent advances in machine learning (ML) and deep learning (DL) have significantly improved the accuracy and reliability of RF-based vital sign monitoring. However, despite their high performance, state-of-the-art DL models are often computationally intensive and energy-demanding, making them unsuitable for continuous use on low-cost, resource-constrained edge devices. Moreover, real-time deployment of such models on embedded platforms remains underexplored in the literature.\r\nTo address this challenge, we propose \\textit{EdgeCaps}, a system that compresses a high-capacity capsule neural network (CapsNet) into a compact student model optimized for edge deployment. Specifically, EdgeCaps distills the knowledge of a 33.7 million-parameter teacher model into a compact student model with only 2.11 million parameters, while maintaining comparable representational capability. Using knowledge distillation (KD)-where a smaller model (student) learns to replicate the outputs of a larger model (teacher)-the student model achieves an accuracy of 85.5%, just two percentage points below the teacher, despite a 94% reduction in parameter count.\r\nWe validate the proposed system through continuous respiration monitoring of five volunteers using a Raspberry Pi 4. A Monte Carlo-based evaluation strategy is employed to measure the generalization and robustness of the results. During deployment, the student model maintains a mean inference latency below 0.3 s, RAM usage around 18 %, and core temperature consistently under 44 °C, all within safe operational limits. In contrast, the teacher model exhibits latencies ranging from 0.4 s to 0.5 s, core temperatures up to 51 °C, and RAM usage up to 22%. These results demonstrate that EdgeCaps enables real-time, energy-efficient, and thermally stable RF-based vital sign monitoring on edge devices without reliance on off-device computation. To our knowledge, this work is the first to introduce a KD-based CapsNet for RF sensing and the first to provide a comprehensive end-to-end evaluation of continuous physiological monitoring on embedded hardware.","manuscriptTitle":"EdgeCaps: Pruned Capsule Networks for RF Vital Sign Measurement in Real-time","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 12:45:36","doi":"10.21203/rs.3.rs-6941397/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-engineering","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commseng","sideBox":"Learn more about [Communications Engineering](http://link.springer.com/journal/44172)","snPcode":"44172","submissionUrl":"https://mts-commseng.nature.com/cgi-bin/main.plex","title":"Communications Engineering","twitterHandle":"@commseng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2470e650-e7fc-4d9a-8e36-824d8cb3e3d9","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":51477720,"name":"Physical sciences/Engineering/Biomedical engineering"},{"id":51477721,"name":"Physical sciences/Engineering/Electrical and electronic engineering"}],"tags":[],"updatedAt":"2025-08-19T12:45:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 12:45:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6941397","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6941397","identity":"rs-6941397","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}