An integrated multi-variable optimization approach to tailor ankle-foot orthosis stiffness to end-user needs

An integrated multi-variable optimization approach to tailor ankle-foot orthosis stiffness to end-user needs | 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 An integrated multi-variable optimization approach to tailor ankle-foot orthosis stiffness to end-user needs Sejin Yi Yoo, Emma A. Gille, Alejandro Dantart, Nikko Van Crey, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7235762/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 Background: Ankle foot orthosis (AFOs) are devices commonly prescribed to assist or rehabilitate gait. A critical parameter influencing their effectiveness is the stiffness of the AFO. Although suppliers typically recommend stiffness levels based on general factors such as body weight and activity level, these guidelines are insufficient to achieve optimal stiffness tailored to each individual. In this work, we introduce an integrated multi-variable optimization approach that simultaneously considers multiple aspects of gait. Unlike previous approaches that rely on a single performance metric (e.g., metabolic cost) or impose a predefined hierarchy among gait parameters, our method makes no assumptions on relative gait domain importance. Furthermore, it allows the inclusion of users’ priorities, enabling a more personalized optimization of AFO stiffness. Methods: Ten children with cerebral palsy (CP) participated in an experimental protocol using the inGAIT-VSO AFO, completing five separate 2-minute walking trials, each with a different stiffness configuration. To determine the optimal stiffness for each participant, our method evaluated performance across five key gait domains: kinematics, spatio-temporal, balance, user perception, and muscular control. Furthermore, we investigated the integration of physiotherapists’ and users’ priorities into the optimization process, and explored the potential of a deep learning model to simplify future data collection needed for the optimization. Results: The proposed optimization method identified the stiffness configuration for each child with CP that most closely aligned their gait to healthy patterns considering the five gait domains. The optimal stiffness varied not only across participants but also across gait domains within the same participant. These findings reveal the importance of having a multi-variable, user-tailored approach. Overall, the inclusion of physiotherapists’ and users’ priorities did not alter the optimal stiffness selection. Conclusion: Our proposed optimization approach opens new possibilities for future research into the personalization and fine-tuning of AFO stiffness. It may also benefit from expanded data collection efforts that enable a more robust evaluation of the proposed deep learning model, supporting its integration into clinical practice. Ankle-foot orthoses Stiffness Optimization Gait Full Text Additional Declarations No competing interests reported. Supplementary Files YiYooetal2025.mov Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Oct, 2025 Reviews received at journal 16 Sep, 2025 Reviews received at journal 10 Sep, 2025 Reviewers agreed at journal 18 Aug, 2025 Reviewers agreed at journal 18 Aug, 2025 Reviewers invited by journal 13 Aug, 2025 Editor assigned by journal 30 Jul, 2025 Submission checks completed at journal 30 Jul, 2025 First submitted to journal 28 Jul, 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-7235762","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502176184,"identity":"405b250e-f342-49fa-96ee-d9302d24c2e0","order_by":0,"name":"Sejin Yi Yoo","email":"","orcid":"","institution":"Center for Automation and Robotics, Spanish National Research Council, CAR-CSIC-UPM","correspondingAuthor":false,"prefix":"","firstName":"Sejin","middleName":"Yi","lastName":"Yoo","suffix":""},{"id":502176185,"identity":"ad8c833a-ea94-43f3-855e-2191b98bde70","order_by":1,"name":"Emma A. Gille","email":"","orcid":"","institution":"University of Twente","correspondingAuthor":false,"prefix":"","firstName":"Emma","middleName":"A.","lastName":"Gille","suffix":""},{"id":502176186,"identity":"5c162ff5-3587-4dc5-a2fb-e409c9d84234","order_by":2,"name":"Alejandro Dantart","email":"","orcid":"","institution":"Center for Automation and Robotics, Spanish National Research Council, CAR-CSIC-UPM","correspondingAuthor":false,"prefix":"","firstName":"Alejandro","middleName":"","lastName":"Dantart","suffix":""},{"id":502176187,"identity":"c6226cc3-0fee-44b4-89a5-1db8772ba5ea","order_by":3,"name":"Nikko Van Crey","email":"","orcid":"","institution":"University of Michigan–Ann Arbor","correspondingAuthor":false,"prefix":"","firstName":"Nikko","middleName":"Van","lastName":"Crey","suffix":""},{"id":502176188,"identity":"74224082-6c80-47f3-b479-61bf3186e3a1","order_by":4,"name":"Elliott J. Rouse","email":"","orcid":"","institution":"University of Michigan–Ann Arbor","correspondingAuthor":false,"prefix":"","firstName":"Elliott","middleName":"J.","lastName":"Rouse","suffix":""},{"id":502176189,"identity":"692e807a-d260-47fa-8539-a6248e2459b1","order_by":5,"name":"Edwin H.F. van Asseldonk","email":"","orcid":"","institution":"University of Twente","correspondingAuthor":false,"prefix":"","firstName":"Edwin","middleName":"H.F. van","lastName":"Asseldonk","suffix":""},{"id":502176190,"identity":"6d842dd4-8fa1-4297-b28e-028e4cd51419","order_by":6,"name":"Cristina Bayón","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYFACHjApx8DA2ECaFmPStSQSrZ6Bv4H34Geeijvp/f2HGz8wVNQR1iJxgC9ZmufMs9wZNxKbJRjOHCasxYCBx0Cat+1w7gYJxjYGxrYDRGkx/g3Ukm7AfxCo5R8RDgNqMQPZkmDAkAjU0sBMWIvEYb40yzlnDhuC/ZJwjAi/8Lf3Hr7xpuKwPH//8YcfPtQQ4TAGoEuYeGCcBCI0gAHjD2JVjoJRMApGwcgEAKXsNPFLJZXdAAAAAElFTkSuQmCC","orcid":"","institution":"Center for Automation and Robotics, Spanish National Research Council, CAR-CSIC-UPM","correspondingAuthor":true,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Bayón","suffix":""}],"badges":[],"createdAt":"2025-07-28 16:09:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7235762/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7235762/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89538695,"identity":"677c1cab-0594-4a14-a608-d0e2c1b74ad7","added_by":"auto","created_at":"2025-08-21 05:55:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":609205,"visible":true,"origin":"","legend":"","description":"","filename":"YiYooetal2025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7235762/v1_covered_cb96e29a-abdb-4214-8baf-b20c0d84c34d.pdf"},{"id":89536558,"identity":"636213dc-c1c3-47ef-9d0d-2ab5b36c68f2","added_by":"auto","created_at":"2025-08-21 05:31:51","extension":"mov","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":46695385,"visible":true,"origin":"","legend":"","description":"","filename":"YiYooetal2025.mov","url":"https://assets-eu.researchsquare.com/files/rs-7235762/v1/c761a680f78c32c2796a5dc6.mov"}],"financialInterests":"No competing interests reported.","formattedTitle":"An integrated multi-variable optimization approach to tailor ankle-foot orthosis stiffness to end-user needs","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":"Ankle-foot orthoses, Stiffness, Optimization, Gait","lastPublishedDoi":"10.21203/rs.3.rs-7235762/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7235762/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Ankle foot orthosis (AFOs) are devices commonly prescribed to assist or rehabilitate gait. A critical parameter influencing their effectiveness is the stiffness of the AFO. Although suppliers typically recommend stiffness levels based on general factors such as body weight and activity level, these guidelines are insufficient to achieve optimal stiffness tailored to each individual. In this work, we introduce an integrated multi-variable optimization approach that simultaneously considers multiple aspects of gait. Unlike previous approaches that rely on a single performance metric (e.g., metabolic cost) or impose a predefined hierarchy among gait parameters, our method makes no assumptions on relative gait domain importance. Furthermore, it allows the inclusion of users’ priorities, enabling a more personalized optimization of AFO stiffness. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: Ten children with cerebral palsy (CP) participated in an experimental protocol using the inGAIT-VSO AFO, completing five separate 2-minute walking trials, each with a different stiffness configuration. To determine the optimal stiffness for each participant, our method evaluated performance across five key gait domains: kinematics, spatio-temporal, balance, user perception, and muscular control. Furthermore, we investigated the integration of physiotherapists’ and users’ priorities into the optimization process, and explored the potential of a deep learning model to simplify future data collection needed for the optimization. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: The proposed optimization method identified the stiffness configuration for each child with CP that most closely aligned their gait to healthy patterns considering the five gait domains. The optimal stiffness varied not only across participants but also across gait domains within the same participant. These findings reveal the importance of having a multi-variable, user-tailored approach. Overall, the inclusion of physiotherapists’ and users’ priorities did not alter the optimal stiffness selection. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: Our proposed optimization approach opens new possibilities for future research into the personalization and fine-tuning of AFO stiffness. It may also benefit from expanded data collection efforts that enable a more robust evaluation of the proposed deep learning model, supporting its integration into clinical practice.\u003c/p\u003e","manuscriptTitle":"An integrated multi-variable optimization approach to tailor ankle-foot orthosis stiffness to end-user needs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 05:31:06","doi":"10.21203/rs.3.rs-7235762/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-03T13:01:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-17T01:14:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-10T18:58:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33678581092024916754988304616042952742","date":"2025-08-18T20:41:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165289825531294719196825898148595253720","date":"2025-08-18T12:30:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-13T15:24:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-30T04:10:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-30T04:10:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of NeuroEngineering and Rehabilitation","date":"2025-07-28T16:01:57+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":"69460cbf-15f2-4e2e-bde0-d900db37dd83","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-01T14:25:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-21 05:31:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7235762","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7235762","identity":"rs-7235762","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}