ConcatPhys: A Dual-Channel Path Data Concatenation Network for Robust Remote Heart Rate Estimation

ConcatPhys: A Dual-Channel Path Data Concatenation Network for Robust Remote Heart Rate Estimation | 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 ConcatPhys: A Dual-Channel Path Data Concatenation Network for Robust Remote Heart Rate Estimation Xiaorui Ge, Jiahe Xing, Bin Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6121383/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Sep, 2025 Read the published version in Machine Vision and Applications → Version 1 posted 9 You are reading this latest preprint version Abstract Facial video-based Blood Volume Pulse (BVP) signal extraction technology has demonstrated significant potential in remote health monitoring. However, most current methods are susceptible to interference from lighting changes and have limited generalization ability in dynamic or complex environments. This paper proposes a dual-channel path data concatenation network called ConcatPhys to improve the accuracy and robustness of remote heart rate (HR) estimation. First, a region-focused block is introduced to concentrate on spatial attention mechanisms, focusing on physiologically relevant regions. This approach effectively uncovers subtle local feature changes and suppresses irrelevant features, reducing sensitivity to background noise and lighting variations. Second, a dual-path framework is constructed for remote photoplethysmography (rPPG) signal prediction. By incorporating dual-path frequency-domain consistency loss and adjacent-frame similarity loss, the network’s anti-interference capability against illumination variations such as lighting changes is strengthened. Finally, leveraging the temporal correlation between adjacent video frames over short intervals, three consecutive feature image segments are concatenated. By averaging the HR values of these three adjacent segments, the video-level HR is computed. This approach enables efficient reconstruction of rPPG signals and accurate HR estimation using only a 6-second facial video segment. Experimental results demonstrate that ConcatPhys achieves state-of-the-art performance across multiple public datasets (VIPL-HR, OBF, UBFC-rPPG), highlighting its significant potential for remote health monitoring applications. Remote health monitoring Deep learning rPPG Visualization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Sep, 2025 Read the published version in Machine Vision and Applications → Version 1 posted Editorial decision: Revision requested 06 May, 2025 Reviews received at journal 20 Mar, 2025 Reviews received at journal 18 Mar, 2025 Reviewers agreed at journal 17 Mar, 2025 Reviewers agreed at journal 17 Mar, 2025 Reviewers invited by journal 17 Mar, 2025 Editor assigned by journal 28 Feb, 2025 Submission checks completed at journal 28 Feb, 2025 First submitted to journal 27 Feb, 2025 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. 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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-6121383","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":422239552,"identity":"0a55f77f-4337-475e-b6e0-cf70e61c2a35","order_by":0,"name":"Xiaorui Ge","email":"","orcid":"","institution":"The Education University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Xiaorui","middleName":"","lastName":"Ge","suffix":""},{"id":422239553,"identity":"25738d73-27e5-4375-baa5-8d4b0f995571","order_by":1,"name":"Jiahe Xing","email":"","orcid":"","institution":"Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jiahe","middleName":"","lastName":"Xing","suffix":""},{"id":422239554,"identity":"f87391cc-9069-4398-a10c-a615ec4a2174","order_by":2,"name":"Bin Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACA2YGxgMJFQzMYB4PkVoYDiScYWDmIV4LEB9gbIOqJkqLOTvvgQMP5x1mt5dIYHzwto1B3pyQFstmvoQDidvSmHkkEpgN57YxGO5sIOSwwzwGQC02IC1s0rxtDAkGB4jSMkcCpIX9NwlaGiC2MBOlxbIZqCXhGNAvZx42S845J2G4gZAWc/4zhg9/1BxOZm9PPvjhTZmNPEFbYCCZgYGxAUhLEKkeCOyIVzoKRsEoGAUjDgAAVfA5i9WuqOIAAAAASUVORK5CYII=","orcid":"","institution":"Xi'an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-02-27 13:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6121383/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6121383/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00138-025-01737-1","type":"published","date":"2025-09-02T15:57:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90828069,"identity":"3c223105-f60b-4158-9c54-beb5afb4ba6d","added_by":"auto","created_at":"2025-09-08 16:05:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":501800,"visible":true,"origin":"","legend":"","description":"","filename":"ConcatPhysADualChannelPathDataConcatenationNetworkforRobustRemoteHeartRateEstimation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6121383/v1_covered_a7aa1264-0ffe-4368-8471-83df7f19c7e1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ConcatPhys: A Dual-Channel Path Data Concatenation Network for Robust Remote Heart Rate Estimation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"machine-vision-and-applications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mvap","sideBox":"Learn more about [Machine Vision and Applications](https://www.springer.com/journal/138)","snPcode":"138","submissionUrl":"https://submission.springernature.com/new-submission/138/3","title":"Machine Vision and Applications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Remote health monitoring, Deep learning, rPPG, Visualization","lastPublishedDoi":"10.21203/rs.3.rs-6121383/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6121383/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFacial video-based Blood Volume Pulse (BVP) signal extraction technology has demonstrated significant potential in remote health monitoring. However, most current methods are susceptible to interference from lighting changes and have limited generalization ability in dynamic or complex environments. This paper proposes a dual-channel path data concatenation network called ConcatPhys to improve the accuracy and robustness of remote heart rate (HR) estimation. First, a region-focused block is introduced to concentrate on spatial attention mechanisms, focusing on physiologically relevant regions. This approach effectively uncovers subtle local feature changes and suppresses irrelevant features, reducing sensitivity to background noise and lighting variations. Second, a dual-path framework is constructed for remote photoplethysmography (rPPG) signal prediction. By incorporating dual-path frequency-domain consistency loss and adjacent-frame similarity loss, the network\u0026rsquo;s anti-interference capability against illumination variations such as lighting changes is strengthened. 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