Predicting Upper-Extremity Function Recovery From Kinematics in Stroke Patients Following Goal-Oriented Computer-Based Training

Predicting Upper-Extremity Function Recovery From Kinematics in Stroke Patients Following Goal-Oriented Computer-Based Training | 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 Predicting Upper-Extremity Function Recovery From Kinematics in Stroke Patients Following Goal-Oriented Computer-Based Training Fabrizio Antenucci, Belén Rubio Ballester, Martina Maier, Anthony C.C. Coolen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-591866/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background: After a stroke, a wide range of deficits can occur with varying onset latencies. As a result, predicting impairment and recovery are enormous challenges in neurorehabilitation. Body function and structure, as well as activities, are assessed using clinical scales. For functional deficits of the upper extremities these include the Fugl-Meyer Assessment for the Upper Extremity (FM-UE), the Chedoke Arm and Hand Activity Inventory (CAHAI) and Barthel Index (BI), administered by clinicians. Although these scales are generally accepted for the evaluation of the motor and functional impairment of the upper-limbs, they are time-consuming, show high inter-rater variability, have low ecological validity, and are vulnerable to biases introduced by compensatory movements and action modifications. For these reasons, alternative methods need to be developed for efficient and objective assessment. Computer-based motion capture and classification tools have the potential to collect and process kinematic data to estimate impairment, function and recovery while overcoming these limitations. Methods: We present a method for estimating clinical scores from movement parameters that are entirely extracted from kinematic data recorded during unsupervised rehabilitation sessions performed with the Rehabilitation Gaming System (RGS). RGS is a rehabilitation technology that uses image-based motion capture, goal-oriented individualised training, virtual reality content delivery, and restricts compensatory trunk movements through feedback. The main protocol considered in this study asks patients to use their upper limbs to intercept spheres that are presented in a 3 dimensional virtual reality display. RGS maps the planar physical arm movements onto matching movements by an avatar presented in a first-person perspective. In this analysis, we performed a multivariate regression using clinical data from 98 stroke patients who completed a total of 191 sessions with RGS. Results: Our multivariate regression model reaches an accuracy of R 2 : 0.38, with an error (σ : 12.8), in predicting FM-UE scores. We analyse our model by assessing reliability (r = 0:89 for test-retest), sensitivity to clinical improvements (95% true positive rate) and generalisation to other tasks that involve planar reaching movements (R 2 : 0.39). The model achieves comparable accuracy also for the CAHAI (R 2 : 0.40) and BI scales (R 2 : 0.35). Conclusions: Our results highlight the clinically relevant predictive power of kinematic data collected during unsupervised goal-oriented motor training combined with automated inference techniques and provide new insight into factors underlying recovery and its biomarkers. Neurology Physical Medicine & Rehab Rehabilitation Stroke Interactive feedback Upper extremities Posture monitoring Motion sensing Motion classification Multivariate regression Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 09 Aug, 2021 Review # 3 received at journal 12 Jul, 2021 Review # 1 received at journal 27 Jun, 2021 Review # 2 received at journal 24 Jun, 2021 Reviewer # 3 agreed at journal 21 Jun, 2021 Reviewer # 2 agreed at journal 20 Jun, 2021 Reviews received at journal 11 Jun, 2021 Reviewer # 1 agreed at journal 11 Jun, 2021 Reviewers invited by journal 08 Jun, 2021 Editor assigned by journal 07 Jun, 2021 Submission checks completed at journal 06 Jun, 2021 Editor invited by journal 06 Jun, 2021 First submitted to journal 04 Jun, 2021 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. 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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-591866","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":32897435,"identity":"d0206e7d-0a8c-41f2-af90-8b82d24a55f0","order_by":0,"name":"Fabrizio Antenucci","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-5070-7408","institution":"CNR: Consiglio Nazionale delle Ricerche","correspondingAuthor":true,"prefix":"","firstName":"Fabrizio","middleName":"","lastName":"Antenucci","suffix":""},{"id":32897436,"identity":"13559535-28b2-42e1-9d0e-fe594653a25d","order_by":1,"name":"Belén Rubio Ballester","email":"","orcid":"","institution":"IBEC: Institut de Bioenginyeria de Catalunya","correspondingAuthor":false,"prefix":"","firstName":"Belén","middleName":"Rubio","lastName":"Ballester","suffix":""},{"id":32897437,"identity":"8f349a51-124c-4958-b0d2-569fb264967b","order_by":2,"name":"Martina Maier","email":"","orcid":"","institution":"IBEC: Institut de Bioenginyeria de Catalunya","correspondingAuthor":false,"prefix":"","firstName":"Martina","middleName":"","lastName":"Maier","suffix":""},{"id":32897438,"identity":"73d9afcc-e316-4d0a-8558-3facb01bba49","order_by":3,"name":"Anthony C.C. Coolen","email":"","orcid":"","institution":"Radboud Universiteit Nijmegen: Radboud Universiteit","correspondingAuthor":false,"prefix":"","firstName":"Anthony","middleName":"C.C.","lastName":"Coolen","suffix":""},{"id":32897439,"identity":"9e8d3ef6-6a4d-4142-8125-3386539c6317","order_by":4,"name":"Paul F. M. J. Verschure","email":"","orcid":"","institution":"IBEC: Institut de Bioenginyeria de Catalunya","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"F. M. J.","lastName":"Verschure","suffix":""}],"badges":[],"createdAt":"2021-06-04 18:29:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-591866/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-591866/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13647966,"identity":"bb69bceb-503f-4594-9648-3d73092743e8","added_by":"auto","created_at":"2021-09-17 09:30:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1760294,"visible":true,"origin":"","legend":"","description":"","filename":"paperClinimetricslegacydata.pdf","url":"https://assets-eu.researchsquare.com/files/rs-591866/v1_covered.pdf"},{"id":10531024,"identity":"6c8a5f27-ae01-40f3-b5a9-0897928cf183","added_by":"auto","created_at":"2021-06-18 14:35:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1756804,"visible":true,"origin":"","legend":"","description":"","filename":"paperClinimetricslegacydata.pdf","url":"https://assets-eu.researchsquare.com/files/rs-591866/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003ePredicting Upper-Extremity Function Recovery From Kinematics in Stroke Patients Following Goal-Oriented Computer-Based Training\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-591866/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":true,"isPdf":false,"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":"Rehabilitation, Stroke, Interactive feedback, Upper extremities, Posture monitoring, Motion sensing, Motion classification, Multivariate regression","lastPublishedDoi":"10.21203/rs.3.rs-591866/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-591866/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: After a stroke, a wide range of deficits can occur with varying onset latencies. As a result, predicting impairment and recovery are enormous challenges in neurorehabilitation. Body function and structure, as well as activities, are assessed using clinical scales. For functional deficits of the upper extremities these include the Fugl-Meyer Assessment for the Upper Extremity (FM-UE), the Chedoke Arm and Hand Activity Inventory (CAHAI) and Barthel Index (BI), administered by clinicians. Although these scales are generally accepted for the evaluation of the motor and functional impairment of the upper-limbs, they are time-consuming, show high inter-rater variability, have low ecological validity, and are vulnerable to biases introduced by compensatory movements and action modifications. For these reasons, alternative methods need to be developed for efficient and objective assessment. Computer-based motion capture and classification tools have the potential to collect and process kinematic data to estimate impairment, function and recovery while overcoming these limitations.\u003c/p\u003e\u003cp\u003eMethods: We present a method for estimating clinical scores from movement parameters that are entirely extracted from kinematic data recorded during unsupervised rehabilitation sessions performed with the Rehabilitation Gaming System (RGS). RGS is a rehabilitation technology that uses image-based motion capture, goal-oriented individualised training, virtual reality content delivery, and restricts compensatory trunk movements through feedback. The main protocol considered in this study asks patients to use their upper limbs to intercept spheres that are presented in a 3 dimensional virtual reality display. RGS maps the planar physical arm movements onto matching movements by an avatar presented in a first-person perspective. In this analysis, we performed a multivariate regression using clinical data from 98 stroke patients who completed a total of 191 sessions with RGS.\u003c/p\u003e\u003cp\u003eResults: Our multivariate regression model reaches an accuracy of R\u003csup\u003e2\u003c/sup\u003e : 0.38, with an error (σ : 12.8), in predicting FM-UE scores. We analyse our model by assessing reliability (r = 0:89 for test-retest), sensitivity to clinical improvements (95% true positive rate) and generalisation to other tasks that involve planar reaching movements (R\u003csup\u003e2\u003c/sup\u003e : 0.39). The model achieves comparable accuracy also for the CAHAI (R\u003csup\u003e2\u003c/sup\u003e : 0.40) and BI scales (R\u003csup\u003e2\u003c/sup\u003e : 0.35).\u003c/p\u003e\u003cp\u003eConclusions: Our results highlight the clinically relevant predictive power of kinematic data collected during unsupervised goal-oriented motor training combined with automated inference techniques and provide new insight into factors underlying recovery and its biomarkers.\u003c/p\u003e","manuscriptTitle":"Predicting Upper-Extremity Function Recovery From Kinematics in Stroke Patients Following Goal-Oriented Computer-Based Training","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-06-18 14:35:38","doi":"10.21203/rs.3.rs-591866/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-08-10T00:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-07-13T00:00:00+00:00","index":3,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"editorInvitedReview","content":"","date":"2021-06-28T00:00:00+00:00","index":1,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"editorInvitedReview","content":"","date":"2021-06-25T00:00:00+00:00","index":2,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"reviewerAgreed","content":"","date":"2021-06-22T00:00:00+00:00","index":3,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2021-06-21T00:00:00+00:00","index":2,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-06-12T00:00:00+00:00","index":0,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2021-06-12T00:00:00+00:00","index":1,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-06-09T00:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-06-07T04:58:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-06-06T23:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-06-06T23:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of NeuroEngineering and Rehabilitation","date":"2021-06-04T11:46:42+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":"51c9e544-ea78-4ca8-bc12-e7cd4f3bfa90","owner":[],"postedDate":"June 18th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":5114553,"name":"Neurology"},{"id":5114554,"name":"Physical Medicine \u0026 Rehab"}],"tags":[],"updatedAt":"2021-12-14T08:55:18+00:00","versionOfRecord":[],"versionCreatedAt":"2021-06-18 14:35:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-591866","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-591866","identity":"rs-591866","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}