Remote photoplethysmography with Anti-Interference Based on Spatio-Temporal Feature Enhancement

Remote photoplethysmography with Anti-Interference Based on Spatio-Temporal Feature Enhancement | 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 Remote photoplethysmography with Anti-Interference Based on Spatio-Temporal Feature Enhancement Dangguo Shao, Jianhua Jin, Lei Ma, Sanli Yi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5351236/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Mar, 2025 Read the published version in Signal, Image and Video Processing → Version 1 posted 5 You are reading this latest preprint version Abstract Remote physiological measurement estimates heart rate (HR) non-contact by analyzing skin color changes from facial videos. This non-intrusive method can be useful in healthcare, border security, and lie detection.These changes are subtle, imperceptible to the naked eye, and easily affected by variations in lighting and motion artifacts from the subject in front of the camera.Traditional methods often use aggressive noise reduction techniques in the presence of real-world noise, which can obscure heart rate information and lead to distorted detection.This study proposes a more effective method for remote heart rate detection that addresses interference by enhancing facial spatial geometric features using the progressive transformation properties of Recursive Spatial Transformation (ReST). It also integrates motion features obtained from optical flow for more stable spatiotemporal feature enhancement. Additionally, we introduce a Multi-Scale Temporal Convolution Module (TDCM) to capture periodic signal changes across different time scales, modeling the periodicity of heart rate signals from various scales to achieve robust recovery of rPPG signals.The entire model has only 30% of the parameters of PhysFormer. Experiments on multiple remote physiological signal measurement datasets demonstrate that the proposed method significantly improves heart rate estimation across various metrics, particularly showing strong robustness in handling videos with severe head movements. Computer vision Remote Photoplethysmography Spatio-temporal fusion TDCM Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Mar, 2025 Read the published version in Signal, Image and Video Processing → Version 1 posted Editorial decision: Revision requested 12 Nov, 2024 Reviewers invited by journal 12 Nov, 2024 Editor assigned by journal 30 Oct, 2024 Submission checks completed at journal 30 Oct, 2024 First submitted to journal 29 Oct, 2024 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-5351236","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372164287,"identity":"e457aed8-a566-4ef9-96eb-400f011b1c74","order_by":0,"name":"Dangguo Shao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Dangguo","middleName":"","lastName":"Shao","suffix":""},{"id":372164288,"identity":"27376ee2-b176-424f-92d5-97cec5f737a8","order_by":1,"name":"Jianhua Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie3RvQrCMBDA8YRCXQKuEbXgAwiBgOBS8U2uuBZcHSOCXeKu6EuI0NUTQRfds4rg5KA4+7mJS+MmmP/+4+44QlyuHyyfU7C83u4BTXpoRwoad8h8T3p6BXZEmOb+RaJkGAvLzYwHyJkfDfTmbI4kDKoqQ9BxH1BwJkfJYFafkJasYQbxShtAELw81du0yAhGaRbxefuET0PnJj7YEcYBFgqh0h3Gvh3hDGFJFUqqV7I+ERa3NNaqdXmS1yt35tgJg0zyMdL2NW/kW+FyuVx/0QMh0kth/By9SAAAAABJRU5ErkJggg==","orcid":"","institution":"Kunming University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Jianhua","middleName":"","lastName":"Jin","suffix":""},{"id":372164289,"identity":"1edf9298-d8ba-4c0f-bcdd-a7df03164d23","order_by":2,"name":"Lei Ma","email":"","orcid":"","institution":"Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Ma","suffix":""},{"id":372164290,"identity":"b01e3d43-7827-4e11-a88f-fcd32a61a745","order_by":3,"name":"Sanli Yi","email":"","orcid":"","institution":"Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Sanli","middleName":"","lastName":"Yi","suffix":""}],"badges":[],"createdAt":"2024-10-29 05:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5351236/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5351236/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11760-024-03809-7","type":"published","date":"2025-03-05T15:56:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78181473,"identity":"7ad8c4ea-e8d8-4453-a010-2b67c3004214","added_by":"auto","created_at":"2025-03-10 17:46:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":628432,"visible":true,"origin":"","legend":"","description":"","filename":"Remotephotoplethysmographyt.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5351236/v1_covered_448e38e3-68e6-4976-a072-9f75d9a468f2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Remote photoplethysmography with Anti-Interference Based on Spatio-Temporal Feature Enhancement","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":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Computer vision, Remote Photoplethysmography, Spatio-temporal fusion, TDCM","lastPublishedDoi":"10.21203/rs.3.rs-5351236/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5351236/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRemote physiological measurement estimates heart rate (HR) non-contact by analyzing skin color changes from facial videos. This non-intrusive method can be useful in healthcare, border security, and lie detection.These changes are subtle, imperceptible to the naked eye, and easily affected by variations in lighting and motion artifacts from the subject in front of the camera.Traditional methods often use aggressive noise reduction techniques in the presence of \u0026nbsp;real-world noise, which can obscure heart rate information and lead to distorted detection.This study proposes a more effective method for remote heart rate detection that addresses interference by enhancing facial spatial geometric features using the progressive transformation properties of Recursive Spatial Transformation (ReST). It also integrates motion features obtained from optical flow for more stable spatiotemporal feature enhancement. 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