Sensitivity of Brain Injury Models to Head Pose, Sample Rate and Interpolation

Sensitivity of Brain Injury Models to Head Pose, Sample Rate and Interpolation | 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 Sensitivity of Brain Injury Models to Head Pose, Sample Rate and Interpolation Thomas Aston, Katie McGill, Filipe Teixeira-Dias This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6029253/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 Purpose: This study investigates how the lower sampling rates and interpolation methods typical of photogrammetric processes for head impact kinematics measurement can affect the accuracy of brain strain predictions, a critical metric in understanding traumatic brain injury (TBI) and mild traumatic brain injury (mTBI). Methods: A dataset of head impact kinematics was generated using emulated instrumented mouthguard (iMG) data sampled at 1000 Hz. These data were converted to head pose measurements and downsampled to rates reflective of video-based systems (500, 100 and 50 Hz). Interpolation schemes (linear, cubic spline and PCHIP) were applied to upsample the downsampled data back to 1000 Hz before recovering angular velocity. Brain strain, measured as the 95th percentile of Maximum Principal Strain (MPS95), was predicted using both a traditional finite element head model (the EdiFEHM) and a convolutional neural network (CNN). Results: Interpolation marginally improved the accuracy of strain predictions at lower sample rates, with PCHIP performing best among the methods tested. However, even with interpolation, over 25% of predictions at 50 Hz deviated by more than 10% from the 1000 Hz reference. The CNN displayed heightened sensitivity to linear interpolation compared to the EdiFEHM, due to its inability to account for the spikes present in the piecewise continuous angular velocity profiles. Conclusion: Higher sample rates (≥ 500 Hz) yield accurate brain strain predictions, but lower rates (e.g., 50 Hz) introduce significant inaccuracies, despite marginal improvements with simple polynomial interpolation. Biomedical Engineering Computational Physics Kinematics Brain strain Sports Finite element modelling Full Text Additional Declarations The authors declare no competing interests. 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. 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-6029253","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":415741810,"identity":"01bc5e87-a77b-450f-8fff-0f36cf6dddd1","order_by":0,"name":"Thomas Aston","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYDACCWTOBwZmMM3YgEcHD1wLG1DlDJK1MPMQo8Veuvnohh9/GOTl5zcfe2xTYc3A336ATXIGPltkjqXd7G1jMNxwjC3dOOdMOoPEmQQ2yQ14HZZjdoO3gYFxAxuPmXRu22EGhhsMbJIPCGi5+ecPg/38NqAWy3+HGeSJ0XKbh40hseEYUAtjw2EGA5AWvA67kZZ2W7ZNInnDsbQ0yZ5j6TyGZxKbLfF5n31G8rGbb/7Y2M5vPnxM4keNtZzc8cMHb/bg0QIFiDTAQyBWRsEoGAWjYBQQAwBzCkZ7oB+ejgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2949-1782","institution":"The University of Edinburgh","correspondingAuthor":true,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Aston","suffix":""},{"id":415741811,"identity":"81139a06-0b7d-4c95-97af-ccda71ab0c50","order_by":1,"name":"Katie McGill","email":"","orcid":"https://orcid.org/0000-0001-5372-2419","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Katie","middleName":"","lastName":"McGill","suffix":""},{"id":415741812,"identity":"d5c5ebda-e605-4fd0-96dc-4579b5c9b480","order_by":2,"name":"Filipe Teixeira-Dias","email":"","orcid":"https://orcid.org/0000-0001-5854-5466","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Filipe","middleName":"","lastName":"Teixeira-Dias","suffix":""}],"badges":[],"createdAt":"2025-02-14 09:34:56","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6029253/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6029253/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76425634,"identity":"d3dcd618-5172-4067-8171-8bb4a69ea3bf","added_by":"auto","created_at":"2025-02-17 05:35:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4317031,"visible":true,"origin":"","legend":"","description":"","filename":"TAetalpreprint.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6029253/v1_covered_148b63fc-42f2-434c-a9a3-57c815bc8907.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eSensitivity of Brain Injury Models to Head Pose, Sample Rate and Interpolation\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"The University of Edinburgh","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Kinematics, Brain strain, Sports, Finite element modelling","lastPublishedDoi":"10.21203/rs.3.rs-6029253/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6029253/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e This study investigates how the lower sampling rates and interpolation methods typical of photogrammetric processes for head impact kinematics measurement can affect the accuracy of brain strain predictions, a critical metric in understanding traumatic brain injury (TBI) and mild traumatic brain injury (mTBI).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA dataset of head impact kinematics was generated using emulated instrumented mouthguard (iMG) data sampled at 1000 Hz. These data were converted to head pose measurements and downsampled to rates reflective of video-based systems (500, 100 and 50 Hz). Interpolation schemes (linear, cubic spline and PCHIP) were applied to upsample the downsampled data back to 1000 Hz before recovering angular velocity. Brain strain, measured as the 95th percentile of Maximum Principal Strain (MPS95), was predicted using both a traditional finite element head model (the EdiFEHM) and a convolutional neural network (CNN).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Interpolation marginally improved the accuracy of strain predictions at lower sample rates, with PCHIP performing best among the methods tested. However, even with interpolation, over 25% of predictions at 50 Hz deviated by more than 10% from the 1000 Hz reference. The CNN displayed heightened sensitivity to linear interpolation compared to the EdiFEHM, due to its inability to account for the spikes present in the piecewise continuous angular velocity profiles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Higher sample rates (≥ 500 Hz) yield accurate brain strain predictions, but lower rates (e.g., 50 Hz) introduce significant inaccuracies, despite marginal improvements with simple polynomial interpolation.\u003c/p\u003e","manuscriptTitle":"Sensitivity of Brain Injury Models to Head Pose, Sample Rate and Interpolation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-17 05:27:46","doi":"10.21203/rs.3.rs-6029253/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aac275bc-3ebc-4077-b850-19e5da22f6eb","owner":[],"postedDate":"February 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44333997,"name":"Biomedical Engineering"},{"id":44333998,"name":"Computational Physics"}],"tags":[],"updatedAt":"2025-02-17T05:27:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-17 05:27:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6029253","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6029253","identity":"rs-6029253","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}