Transformative Role of Artificial Intelligence and Machine Learning in Rehabilitation Engineering; A Systematic Review

Transformative Role of Artificial Intelligence and Machine Learning in Rehabilitation Engineering; A Systematic Review | 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 Systematic Review Transformative Role of Artificial Intelligence and Machine Learning in Rehabilitation Engineering; A Systematic Review Dr. Jose Luis Contreras-Vidal, Gregory Brusola, Muhammad Mansoor Mughal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9518032/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 Objective : The field of rehabilitation engineering has evolved to incorporate Artificial Intelligence (AI) and Machine Learning (ML) into technologies and tools designed to assist and promote the rehabilitation of individuals suffering from stroke, paralysis, and other clinical conditions. Here, a systematic review was done to understand the opportunities, challenges, gaps, deficiencies, inconsistencies, and trends in the deployment of AI/ML in rehabilitation engineering. Approach : A search in IEEE Xplore, Web of Science and PubMed databases for the period 2011 to 2021 resulted in 110 identified studies that met the inclusion criteria. Findings are summarized with respect to clinical population, number of participants, rehabilitation task, rehabilitation device, model inputs, duration of intervention, AI/ML algorithm, model accuracy, clinical outcome and presence of feedback. Main Results : Most studies (63%) focused on upper limb or hand rehabilitation with 27% addressing lower-limb rehabilitation. Stroke had the largest representation (58%) among the clinical populations studied followed by spinal cord injury (20%). Classical ML algorithms accounted for 90% of the studies, followed by deep learning (DL). About half of the studies used electroencephalography (EEG) as input to the AI/ML model. The output of these models were used to control different types of virtual and physical devices. Although not all the studies reported the model accuracy, for those that reported results the accuracy ranged from 42% - 100%. Clinical outcome measures were not included in 66% of the studies. Less than one-fifth of the studies had open-loop protocols. Unfortunately, 18% of the studies were single session, with 26% reporting between 2-10 sessions. Significance : Future studies should 1) investigate applications of DL in the rehabilitation of motor function; 2) be closed-loop and 3) include multiple sessions to understand the longitudinal effects of therapy. Moreover, studies should address model explainability, and follow standard engineering, clinical, and end-user metrics. artificial intelligence machine learning rehabilitation stroke electroen- cephalography (EEG) spinal cord injury (SCI) 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. 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-9518032","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":629068098,"identity":"b46fb37f-7cd8-4907-bfcf-187ad41888a8","order_by":0,"name":"Dr. Jose Luis Contreras-Vidal","email":"","orcid":"","institution":"University of Houston","correspondingAuthor":false,"prefix":"Dr.","firstName":"Jose","middleName":"Luis","lastName":"Contreras-Vidal","suffix":""},{"id":629068099,"identity":"802effd9-4d38-410c-b0c3-427e13ce1467","order_by":1,"name":"Gregory Brusola","email":"","orcid":"","institution":"University of Texas Medical Branch","correspondingAuthor":false,"prefix":"","firstName":"Gregory","middleName":"","lastName":"Brusola","suffix":""},{"id":629167444,"identity":"34643523-a84b-4e8a-b0f3-838a7ddb917f","order_by":2,"name":"Muhammad Mansoor Mughal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYJACZgYGCzlkATaIIH4tEsZwHg+xWhIbiNaiO+2M8euCGon0/vYe4xc/99yRs2fgMXvAUGGNMAQNmN3OMbOecUwid8aZM2aWPc+eGfMw8JgbMJxJx6vFmIdNIneDRI6ZAc+Bw4k9QFskGNsOE9DyTyLdAKjF8M+Bw/UQLf/wajF+zNsmkQDUYvwYaEsCD1hLAz4taWXMvH0ShjPOHCtjljlw2LDnMFuZRMKxdGPcWpI3f+b5ZiPP3968+eObA4fl2dubt0l8qLGWxaUFCNgkUBmgGEnArRys5AM6YxSMglEwCkYBCgAAuZdTvVW2YvUAAAAASUVORK5CYII=","orcid":"","institution":"University of Houston","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Mansoor","lastName":"Mughal","suffix":""}],"badges":[],"createdAt":"2026-04-24 13:54:33","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-9518032/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9518032/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107870424,"identity":"f611a996-b73b-489b-8e7d-271cc3825ce5","added_by":"auto","created_at":"2026-04-27 07:39:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":653413,"visible":true,"origin":"","legend":"","description":"","filename":"ArtificialIntelligenceandMachineLearninginRehabilitationJULY2025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9518032/v1_covered_d062657a-66ef-45dd-9786-50c004d64e14.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eTransformative Role of Artificial Intelligence and Machine Learning in Rehabilitation Engineering; A Systematic Review\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Houston","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":"artificial intelligence, machine learning, rehabilitation, stroke, electroen- cephalography (EEG), spinal cord injury (SCI)","lastPublishedDoi":"10.21203/rs.3.rs-9518032/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9518032/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/em\u003e: The field of rehabilitation engineering has evolved to incorporate Artificial Intelligence (AI) and Machine Learning (ML) into technologies and tools designed to assist and promote the rehabilitation of individuals suffering from stroke, paralysis, and other clinical conditions. Here, a systematic review was done to understand the opportunities, challenges, gaps, deficiencies, inconsistencies, and trends in the deployment of AI/ML in rehabilitation engineering.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eApproach \u003c/strong\u003e\u003c/em\u003e: A search in IEEE Xplore, Web of Science and PubMed databases for the period 2011 to 2021 resulted in 110 identified studies that met the inclusion criteria. Findings are summarized with respect to clinical population, number of participants, rehabilitation task, rehabilitation device, model inputs, duration of intervention, AI/ML algorithm, model accuracy, clinical outcome and presence of feedback.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMain Results\u003c/strong\u003e\u003c/em\u003e: Most studies (63%) focused on upper limb or hand rehabilitation with 27% addressing lower-limb rehabilitation. Stroke had the largest representation (58%) among the clinical populations studied followed by spinal cord injury (20%). Classical ML algorithms accounted for 90% of the studies, followed by deep learning (DL). About half of the studies used electroencephalography (EEG) as input to the AI/ML model. The output of these models were used to control different types of virtual and physical devices. Although not all the studies reported the model accuracy, for those that reported results the accuracy ranged from 42% - 100%. Clinical outcome measures were not included in 66% of the studies. Less than one-fifth of the studies had open-loop protocols. Unfortunately, 18% of the studies were single session, with 26% reporting between 2-10 sessions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSignificance\u003c/strong\u003e\u003c/em\u003e: Future studies should 1) investigate applications of DL in the rehabilitation of motor function; 2) be closed-loop and 3) include multiple sessions to understand the longitudinal effects of therapy. 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