A Medical Device System for Multi-Modal Pain Assessment Using Facial Expression Recognition and Surface Electromyography

A Medical Device System for Multi-Modal Pain Assessment Using Facial Expression Recognition and Surface Electromyography | 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 A Medical Device System for Multi-Modal Pain Assessment Using Facial Expression Recognition and Surface Electromyography Xin Liu, Wenrui Zhu, Xingui Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9310940/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Automated pain monitoring devices are critical for effective patient care, particularly for non-communicative individuals in intensive care and post-operative settings. This paper presents the development of a medical device system for objective pain assessment that integrates facial expression recognition and surface electromyography (sEMG) signal processing. The system is designed as a clinically-deployable device featuring a nine-level hierarchical warning mechanism for real-time pain monitoring. The device architecture consists of three modules: (1) a facial video acquisition unit using standard webcam technology, (2) a wireless sEMG signal recording unit, and (3) a central processing unit implementing a hybrid fusion model. The fusion model dynamically weights facial expression indicators (derived from Facial Action Coding System analysis) and sEMG features (time-domain Root Mean Square and frequency-domain Mean Power Frequency) to determine pain severity. Experimental validation with 45 subjects demonstrated an overall accuracy of 95.6%, significantly outperforming unimodal approaches (93.3% for both facial-only and sEMG-only configurations). The device achieves real-time processing with an average latency of 1.2 seconds, meeting clinical deployment requirements. This medical device system offers a cost-effective, automated solution for continuous pain monitoring, facilitating timely medical intervention and enhancing precision care capabilities in healthcare environments. Physical sciences/Engineering Health sciences/Health care Health sciences/Medical research medical device system pain assessment facial expression recognition surface electromyography multi-modal fusion clinical deployment healthcare monitoring Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 17 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 16 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Editor invited by journal 13 Apr, 2026 Submission checks completed at journal 08 Apr, 2026 First submitted to journal 08 Apr, 2026 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-9310940","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":624618004,"identity":"92530ad7-fe25-4f01-9d3a-4b7256062e64","order_by":0,"name":"Xin Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACfmbmAwYfKmzkIFw2IrRItrclFM44k2bMwMBMpBaDM2cMPnO2HE5sIFoLw40Ew82MDWnp2yXyDzB8KDvMwD+7Ab8OxhkJycaFO2xyd85IBnLOHWaQuHMAvxZmiYRjxjPPpOVuuJHMwMzbdpjBQCIBvxY2icT230CV6QYgLX+J0cLDc5jBGKglAayFkRgtEuxtDIbAQDbccOaxwcGec+k8EjcIaLE/zP8BFJXyBscTHz74UWYtxz+DgBYUcADkUhLUj4JRMApGwSjABQDkMUdZ4k15kwAAAABJRU5ErkJggg==","orcid":"","institution":"Nanchang Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Xin","middleName":"","lastName":"Liu","suffix":""},{"id":624618006,"identity":"2543c7ef-6946-4c0d-9113-970070e791d0","order_by":1,"name":"Wenrui Zhu","email":"","orcid":"","institution":"Nanchang Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Wenrui","middleName":"","lastName":"Zhu","suffix":""},{"id":624618007,"identity":"a92f4380-3ada-4d1e-a4b9-f3ff6e8b7e3e","order_by":2,"name":"Xingui Li","email":"","orcid":"","institution":"Nanchang Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xingui","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-04-03 08:54:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9310940/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9310940/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107707295,"identity":"abacd9fb-ed87-43bc-ad55-69b513619442","added_by":"auto","created_at":"2026-04-24 09:20:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":515056,"visible":true,"origin":"","legend":"","description":"","filename":"JournalofMedicalSystems3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9310940/v1_covered_606ef69a-736c-4c2d-baed-0f5918fc00fb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Medical Device System for Multi-Modal Pain Assessment Using Facial Expression Recognition and Surface Electromyography","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"medical device system, pain assessment, facial expression recognition, surface electromyography, multi-modal fusion, clinical deployment, healthcare monitoring ","lastPublishedDoi":"10.21203/rs.3.rs-9310940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9310940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Automated pain monitoring devices are critical for effective patient care, particularly for non-communicative individuals in intensive care and post-operative settings. This paper presents the development of a medical device system for objective pain assessment that integrates facial expression recognition and surface electromyography (sEMG) signal processing. The system is designed as a clinically-deployable device featuring a nine-level hierarchical warning mechanism for real-time pain monitoring. The device architecture consists of three modules: (1) a facial video acquisition unit using standard webcam technology, (2) a wireless sEMG signal recording unit, and (3) a central processing unit implementing a hybrid fusion model. The fusion model dynamically weights facial expression indicators (derived from Facial Action Coding System analysis) and sEMG features (time-domain Root Mean Square and frequency-domain Mean Power Frequency) to determine pain severity. Experimental validation with 45 subjects demonstrated an overall accuracy of 95.6\\%, significantly outperforming unimodal approaches (93.3\\% for both facial-only and sEMG-only configurations). The device achieves real-time processing with an average latency of 1.2 seconds, meeting clinical deployment requirements. 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