Biomechanical behavior of lower limb joints based on Time-series k- means clustering Algorithm during Gait

Biomechanical behavior of lower limb joints based on Time-series k- means clustering Algorithm during Gait | 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 Article Biomechanical behavior of lower limb joints based on Time-series k- means clustering Algorithm during Gait Rozhin Molavian, Ali Fatahi, Davood Khezri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4519492/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 Given the inherent essence of gait, there is potential to analyze and categorize this data using artificial intelligence. Aim of this study Investigate the mechanical properties of lower limb articulations while walking through the application of artificial intelligence methodologies. Twenty-eight young individuals were part of a study. They completed a task walking 10 meters on a path. The task was done twice, once at their own speed and once at 100 beats per minute. Data points were categorized using a machine learning technique called time-series k-means clustering. The movements of hip, knee, and ankle joints were compared across different clusters. Statistical analysis was used to find any differences among the groups. Results of the study shows that angular velocity and joint moment influence subject distribution in clusters. Cluster 3 with the smallest population has the highest mechanical power. Mechanical power of ankle and hip joints affects clustering pattern. Second group has few participants with random power allocation among joints. Knee joint is the primary determinant in clustering process. Hip joint has a minor role in power generation and absorption at preferred speed. Knee and ankle joints are crucial in clustering outcomes in all scenarios. The findings indicate notable disparities between the production and absorption stages of walking in situations involving preferred and constant speeds, with the exception of the hip joint. Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Computational biology and bioinformatics/Data processing Biological sciences/Computational biology and bioinformatics/Machine learning Health sciences/Health care/Paediatrics Health sciences/Health care/Public health Artificial Intelligence (AI) Gait Biomechanics machine learning (ML) Time-series clustering Full Text Additional Declarations No competing interests reported. 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-4519492","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":315225199,"identity":"359cb646-9b26-4cb2-9b23-280116db68ed","order_by":0,"name":"Rozhin Molavian","email":"","orcid":"","institution":"Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"Rozhin","middleName":"","lastName":"Molavian","suffix":""},{"id":315225200,"identity":"d1c75448-3ac4-43ea-9dc5-14d7f6de57b0","order_by":1,"name":"Ali Fatahi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACdsYGhgogzcbe2ADiJzAgSByAGajlDIMBAxvPQZAWA2K0ADFIC4MEWJkBXsVgwN/M3MBwoOaPPJ/k48bPPDV/8hjYDz9geLgHtxaJw0CHHThmYNgmndgszXPMoJiBJ82AIeEZHmuAWpg/sBkwArU0SPM2GCQ2MOQA/XIAtw55sC3/DOzbJA82/wZr4X+DX4sBSMvBNoPENgmgRWAtEgRsMQRqOXCwzzi5jSexzXLOMeNiNolnBgfwaZE73v7wwYFvcrbz248/vvGmRi6Pnz/54cMfeLSAAKo0G4bIKBgFo2AUjAKSAQButlBp/HCp3gAAAABJRU5ErkJggg==","orcid":"","institution":"Islamic Azad University","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"","lastName":"Fatahi","suffix":""},{"id":315225201,"identity":"7cfcc474-a7cc-491a-8d41-76295f2b2d02","order_by":2,"name":"Davood Khezri","email":"","orcid":"","institution":"Sport Science Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Davood","middleName":"","lastName":"Khezri","suffix":""}],"badges":[],"createdAt":"2024-06-03 05:51:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4519492/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4519492/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67130328,"identity":"73ef7258-e6fa-451a-b0fa-335106caece6","added_by":"auto","created_at":"2024-10-21 12:40:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":352592,"visible":true,"origin":"","legend":"","description":"","filename":"final2jun2024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4519492/v1_covered_371317a4-577d-4233-b6ce-d65202075858.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Biomechanical behavior of lower limb joints based on Time-series k- means clustering Algorithm during Gait","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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 (AI), Gait, Biomechanics, machine learning (ML), Time-series clustering","lastPublishedDoi":"10.21203/rs.3.rs-4519492/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4519492/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Given the inherent essence of gait, there is potential to analyze and categorize this data using artificial intelligence. 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