Application of biofeedback loop in rehabilitation optimization after robot-assisted total knee replacement

Application of biofeedback loop in rehabilitation optimization after robot-assisted total knee replacement | 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 Application of biofeedback loop in rehabilitation optimization after robot-assisted total knee replacement Ting Jiang, Mingzhu Zhang, Shiguo Gong, Hui Deng, Ying Xu, Jianying Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8657711/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 Objective:Robot-assisted total knee replacement (TKA) faces technical gaps including lack of real-time quantitative feedback and insufficient individualization in postoperative rehabilitation. This study aims to explore the application value of a multimodal biofeedback loop system—integrating joint pressure sensing, surface electromyography (sEMG) monitoring, and kinematic tracking—in optimizing postoperative rehabilitation, thereby improving functional recovery efficiency and prosthesis long-term survival rate. Methods: A single-center randomized controlled trial was conducted at Jiujiang First People's Hospital from January 2022 to October 2025, enrolling 160 robot-assisted TKA patients. They were randomly assigned to the observation group (biofeedback loop-assisted rehabilitation, n=80) and the control group (traditional rehabilitation, n=80). The observation group adopted a closed-loop system with 16-channel sEMG array, piezoresistive pressure sensors, and inertial measurement units (IMU), combined with a lightweight convolutional neural network (CNN) for real-time data analysis (<=200 ms delay) and dynamic feedback (vibration/visual/auditory). The control group received conventional rehabilitation. Outcomes were compared at 12 weeks, 6 months, and 1 year postoperatively using Knee Association Scoring (KSS), gait analysis, imaging, and cost accounting. Results: Preliminary data showed that the observation group had significantly higher KSS scores at 12 weeks postoperatively than the control group (P<0.05), with 30% shorter recovery time, 35% reduced abnormal prosthesis stress, and 20%-25% lower overall treatment cost. At 6 months, the observation group exhibited better gait symmetry and muscle activation efficiency (P<0.05). The biofeedback system achieved high data synchronization accuracy (<5 ms error) and algorithmic reliability (RMSE=0.03±0.01 for muscle activation calculation). Conclusion: The multimodal biofeedback loop system optimizes robot-assisted TKA postoperative rehabilitation through real-time multimodal data fusion and dynamic feedback. It not only improves functional recovery quality and economic efficiency but also fills the technical gap between robotic surgical precision and postoperative rehabilitation. With strong reproducibility and clinical promotability, this system provides an effective solution for precise rehabilitation, warranting clinical application. biofeedback loop robot-assisted total knee replacement postoperative rehabilitation randomized controlled trial functional recovery Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 May, 2026 Reviewers agreed at journal 16 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 07 Feb, 2026 Reviewers invited by journal 02 Feb, 2026 Editor assigned by journal 25 Jan, 2026 Submission checks completed at journal 25 Jan, 2026 First submitted to journal 21 Jan, 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-8657711","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584840238,"identity":"47cacdae-6ae9-4733-a125-03459a55d7ae","order_by":0,"name":"Ting Jiang","email":"","orcid":"","institution":"Jiujiang First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Jiang","suffix":""},{"id":584840239,"identity":"6783e7eb-099a-4c9b-a49b-41bac2fbcd33","order_by":1,"name":"Mingzhu 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09:32:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8657711/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8657711/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101943305,"identity":"db15f122-0cd7-4f08-9fc4-7f618fbb9adb","added_by":"auto","created_at":"2026-02-05 09:41:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":766246,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8657711/v1_covered_ec70ec3a-c652-4c7b-8dcd-fb4751fb28aa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of biofeedback loop in rehabilitation optimization after robot-assisted total knee replacement","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":"biomedical-engineering-online","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmeo","sideBox":"Learn more about [BioMedical Engineering OnLine](http://biomedical-engineering-online.biomedcentral.com/)","snPcode":"12938","submissionUrl":"https://submission.nature.com/new-submission/12938/3","title":"BioMedical Engineering OnLine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"biofeedback loop, robot-assisted total knee replacement, postoperative rehabilitation, randomized controlled trial, functional recovery","lastPublishedDoi":"10.21203/rs.3.rs-8657711/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8657711/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective:Robot-assisted total knee replacement (TKA) faces technical gaps including lack of real-time quantitative feedback and insufficient individualization in postoperative rehabilitation. This study aims to explore the application value of a multimodal biofeedback loop system—integrating joint pressure sensing, surface electromyography (sEMG) monitoring, and kinematic tracking—in optimizing postoperative rehabilitation, thereby improving functional recovery efficiency and prosthesis long-term survival rate.\u003c/p\u003e\n\u003cp\u003eMethods: A single-center randomized controlled trial was conducted at Jiujiang First People's Hospital from January 2022 to October 2025, enrolling 160 robot-assisted TKA patients. They were randomly assigned to the observation group (biofeedback loop-assisted rehabilitation, n=80) and the control group (traditional rehabilitation, n=80). The observation group adopted a closed-loop system with 16-channel sEMG array, piezoresistive pressure sensors, and inertial measurement units (IMU), combined with a lightweight convolutional neural network (CNN) for real-time data analysis (\u0026lt;=200 ms delay) and dynamic feedback (vibration/visual/auditory). The control group received conventional rehabilitation. Outcomes were compared at 12 weeks, 6 months, and 1 year postoperatively using Knee Association Scoring (KSS), gait analysis, imaging, and cost accounting.\u003c/p\u003e\n\u003cp\u003eResults: Preliminary data showed that the observation group had significantly higher KSS scores at 12 weeks postoperatively than the control group (P\u0026lt;0.05), with 30% shorter recovery time, 35% reduced abnormal prosthesis stress, and 20%-25% lower overall treatment cost. At 6 months, the observation group exhibited better gait symmetry and muscle activation efficiency (P\u0026lt;0.05). The biofeedback system achieved high data synchronization accuracy (\u0026lt;5 ms error) and algorithmic reliability (RMSE=0.03±0.01 for muscle activation calculation).\u003c/p\u003e\n\u003cp\u003eConclusion: The multimodal biofeedback loop system optimizes robot-assisted TKA postoperative rehabilitation through real-time multimodal data fusion and dynamic feedback. It not only improves functional recovery quality and economic efficiency but also fills the technical gap between robotic surgical precision and postoperative rehabilitation. With strong reproducibility and clinical promotability, this system provides an effective solution for precise rehabilitation, warranting clinical application.\u003c/p\u003e","manuscriptTitle":"Application of biofeedback loop in rehabilitation optimization after robot-assisted total knee replacement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-04 18:59:44","doi":"10.21203/rs.3.rs-8657711/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-17T01:49:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114511308080072877120530983887555850216","date":"2026-05-17T01:13:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315118849473839971983563012063495995957","date":"2026-05-13T07:03:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231243331200278848423420993393967537451","date":"2026-05-12T14:03:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88581249757335363011346689766349431992","date":"2026-02-08T00:16:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-03T00:10:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-26T03:02:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-26T03:01:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BioMedical Engineering OnLine","date":"2026-01-21T08:45:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biomedical-engineering-online","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmeo","sideBox":"Learn more about [BioMedical Engineering OnLine](http://biomedical-engineering-online.biomedcentral.com/)","snPcode":"12938","submissionUrl":"https://submission.nature.com/new-submission/12938/3","title":"BioMedical Engineering OnLine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4b50f535-d04a-41b2-b379-1714ad600932","owner":[],"postedDate":"February 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-17T01:49:25+00:00","index":49,"fulltext":""},{"type":"reviewerAgreed","content":"114511308080072877120530983887555850216","date":"2026-05-17T01:13:51+00:00","index":48,"fulltext":""},{"type":"reviewerAgreed","content":"315118849473839971983563012063495995957","date":"2026-05-13T07:03:22+00:00","index":47,"fulltext":""},{"type":"reviewerAgreed","content":"231243331200278848423420993393967537451","date":"2026-05-12T14:03:55+00:00","index":46,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-04T18:59:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-04 18:59:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8657711","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8657711","identity":"rs-8657711","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}