Reducing Robotic Upper-Limb Assessment Time While Maintaining Precision: A Time Series Foundation Model Approach

Reducing Robotic Upper-Limb Assessment Time While Maintaining Precision: A Time Series Foundation Model Approach | 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 Reducing Robotic Upper-Limb Assessment Time While Maintaining Precision: A Time Series Foundation Model Approach Faranak Akbarifar, Nooshin Maghsoodi, Sean P. Dukelow, Stephen H. Scott, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8040626/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 Purpose: Visually Guided Reaching (VGR) on the Kinarm robot yields sensitive kinematic biomarkers but requires 40–64 reaches, imposing time and fatigue burdens. We evaluate whether time series foundation models can replace unrecorded trials from an early subset of reaches while preserving reliability of standard Kinarm parameters. Methods: We analyzed VGR speed signals from 461 stroke, and 599 control participants across 4- and 8-target reaching protocols. We withheld all but the first 8 or 16 reaching trials and used ARIMA, MOMENT, and Chronos models, fine-tuned on 70% of participants, to forecast synthetic trials. We recomputed four kinematic features of reaching (reaction time, movement time, posture speed, max speed) on combined recorded plus forecasted trials and compared to full-length references using ICC(2,1). Results: Chronos forecasts restored ICC ( \((\geq 0.90)\) ) for all parameters with only 8 real trials plus forecasts, matching the reliability of 24–28 recorded reaches ( \((\Delta ICC \leq 0.07)\) ). MOMENT yielded intermediate gains, while ARIMA improvements were minimal. Across cohorts and protocols, synthetic trials replaced reaches without significantly compromising feature reliability. Conclusion: Foundation-model forecasting can greatly shorten Kinarm VGR assessment time. For the most impaired stroke survivors, sessions drop from 4–5 minutes to about 1 minute while preserving kinematic precision. This forecast-augmented paradigm promises efficient robotic evaluations for assessing motor impairments following stroke. Kinarm assessment Visually guided reaching time series forecasting foundation models Full Text Additional Declarations Competing interest reported. SHS is co-founder and CSO of Kinarm (formally known as BKIN Technologies), the company that commercializes the robotic technology used in the present study. All other authors confirm no conflict of interest. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Jan, 2026 Reviews received at journal 19 Dec, 2025 Reviews received at journal 15 Dec, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers agreed at journal 22 Nov, 2025 Reviewers invited by journal 20 Nov, 2025 Editor assigned by journal 06 Nov, 2025 Submission checks completed at journal 06 Nov, 2025 First submitted to journal 05 Nov, 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. 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-8040626","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551438371,"identity":"80d79214-cbb7-4f6e-81b6-628c1ecad1a4","order_by":0,"name":"Faranak Akbarifar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYBAC9gYGZmYgzQNkMD4gSgvPAagWEMOAJC0gBpsEcVoYmB8bF9Tck+FhP/us4m2bjZw5/xkDhh81+LSwGSfPOFbMw8OTbnZzbluaseWMHAPGnmO4tdgzMBgf5mFL4LFnSGO7zdt2OHHDDR4DZgY2fLawfz7M8y+Bh4f/GVsxb9v/+g3nzwC1/MOnhcc4mbcNqEUijY2Zt+1AgsGBHANmxjY8Wph5io1n9oG0PGOWnHMu2XDDjbSCg719eLSwt2+WLviWYM/Dn8b44U2ZnbzB+cMbH/z4hlsLAzOqMyHgAB4N6HYSr3QUjIJRMApGEAAAIYFFbmqUpjIAAAAASUVORK5CYII=","orcid":"","institution":"Queen's University","correspondingAuthor":true,"prefix":"","firstName":"Faranak","middleName":"","lastName":"Akbarifar","suffix":""},{"id":551438372,"identity":"b50438a1-4f0b-4f7e-9d33-c1df5e12c7d9","order_by":1,"name":"Nooshin Maghsoodi","email":"","orcid":"","institution":"Queen's University","correspondingAuthor":false,"prefix":"","firstName":"Nooshin","middleName":"","lastName":"Maghsoodi","suffix":""},{"id":551438373,"identity":"c6e766dc-749a-4a26-8f18-8ba177930e83","order_by":2,"name":"Sean P. 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SHS is co-founder and CSO of Kinarm (formally known as BKIN Technologies), the company that commercializes the robotic technology used in the present study. All other authors confirm no conflict of interest.","formattedTitle":"Reducing Robotic Upper-Limb Assessment Time While Maintaining Precision: A Time Series Foundation Model Approach","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":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Kinarm assessment, Visually guided reaching, time series forecasting, foundation models","lastPublishedDoi":"10.21203/rs.3.rs-8040626/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8040626/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose:\u003c/h2\u003e\u003cp\u003eVisually Guided Reaching (VGR) on the Kinarm robot yields sensitive kinematic biomarkers but requires 40\u0026ndash;64 reaches, imposing time and fatigue burdens. We evaluate whether time series foundation models can replace unrecorded trials from an early subset of reaches while preserving reliability of standard Kinarm parameters.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eWe analyzed VGR speed signals from 461 stroke, and 599 control participants across 4- and 8-target reaching protocols. We withheld all but the first 8 or 16 reaching trials and used ARIMA, MOMENT, and Chronos models, fine-tuned on 70% of participants, to forecast synthetic trials. We recomputed four kinematic features of reaching (reaction time, movement time, posture speed, max speed) on combined recorded plus forecasted trials and compared to full-length references using ICC(2,1).\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eChronos forecasts restored ICC (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\geq 0.90)\\)\u003c/span\u003e\u003c/span\u003e) for all parameters with only 8 real trials plus forecasts, matching the reliability of 24\u0026ndash;28 recorded reaches (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\Delta ICC \\leq 0.07)\\)\u003c/span\u003e\u003c/span\u003e). MOMENT yielded intermediate gains, while ARIMA improvements were minimal. Across cohorts and protocols, synthetic trials replaced reaches without significantly compromising feature reliability.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eFoundation-model forecasting can greatly shorten Kinarm VGR assessment time. For the most impaired stroke survivors, sessions drop from 4\u0026ndash;5 minutes to about 1 minute while preserving kinematic precision. This forecast-augmented paradigm promises efficient robotic evaluations for assessing motor impairments following stroke.\u003c/p\u003e","manuscriptTitle":"Reducing Robotic Upper-Limb Assessment Time While Maintaining Precision: A Time Series Foundation Model Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-27 22:42:51","doi":"10.21203/rs.3.rs-8040626/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-12T04:18:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-19T17:48:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T02:36:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325344933486962602776353189374712552456","date":"2025-11-25T18:55:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321936564616401358115995375782767176042","date":"2025-11-22T08:36:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-20T08:10:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-06T23:38:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-06T23:37:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of NeuroEngineering and Rehabilitation","date":"2025-11-05T16:45:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7989ee2b-0a4a-4ca4-9442-bff7d4abb717","owner":[],"postedDate":"November 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T09:55:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-27 22:42:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8040626","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8040626","identity":"rs-8040626","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}