Personalized Kinematic Constraint-Based Drift Correction for 3D Golf Swing Trajectory Monitoring with a Wrist-Worn IMU

Personalized Kinematic Constraint-Based Drift Correction for 3D Golf Swing Trajectory Monitoring with a Wrist-Worn IMU | 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 Personalized Kinematic Constraint-Based Drift Correction for 3D Golf Swing Trajectory Monitoring with a Wrist-Worn IMU Guena Park, Sukyung Park This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9318966/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Monitoring three-dimensional (3D) golf swing trajectories is important for performance analysis, yet accurate measurement still relies largely on laboratory-based motion capture systems. Wrist-worn inertial measurement units (IMU) provide a practical alternative, but trajectory estimation is limited by orientation errors and integration drift. Existing drift correction approaches typically rely on predefined kinematic constraints that are not tailored to individual swing mechanics, despite the substantial inter-individual variability observed in golf swings. In this study, we propose a subject-specific drift correction framework for 3D golf swing trajectory monitoring using a wrist-worn IMU. The relative wrist displacement between address and impact was defined as a subject-specific kinematic constraint. This constraint was estimated from monocular video by compensating for depth ambiguity using club length as a geometric constraint. A bidirectional long short-term memory (BiLSTM) model was then trained to infer the same constraint from IMU signals, allowing subsequent drift correction using IMU data alone. The proposed method reduced entire swing and downswing trajectory errors relative to motion capture from approximately 68 cm and 78 cm for uncorrected IMU integration to approximately 20 cm and 15 cm, respectively. Furthermore, a trajectory consistency metric computed from the corrected IMU-estimated trajectories showed high linear agreement with the motion capture-based reference for intentionally faster than preferred swings (R = 0.86, p < 0.05), indicating the feasibility of monitoring relative changes in swing consistency with a single wrist-worn IMU. These findings suggest that a biomechanics-informed kinematic constraint, namely the within-subject consistency of address-to-impact wrist displacement, can serve as a compact learning target for IMU drift correction. By grounding personalization in a domain-specific prior rather than relying on generalized models, the framework reduces reliance on motion capture-based large scale trajectory labels and enables subject-specific calibration from a monocular camera session. This framework may be extended to other sports movements in which stable within-subject kinematic regularity can be identified. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers invited by journal 30 Apr, 2026 Editor assigned by journal 30 Apr, 2026 Editor invited by journal 20 Apr, 2026 Submission checks completed at journal 13 Apr, 2026 First submitted to journal 13 Apr, 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. 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-9318966","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":635205400,"identity":"4c26ffbc-ad9b-4790-91cc-0fc424ad3f40","order_by":0,"name":"Guena Park","email":"","orcid":"","institution":"Korea Advanced Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Guena","middleName":"","lastName":"Park","suffix":""},{"id":635205401,"identity":"74399622-734c-4d96-b068-9949aede4d7b","order_by":1,"name":"Sukyung Park","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYFACNgjFz8PAcABFhKAWyR6QlgRStBicAZHEaJGfkZYmzbujNs/4zOGHB3/+YJDnb2BL+4BPi8GNtGPSvGeOF5udbTM4zJPAYDjjANvhGXi1SKS3SfO2HUvcdp7B4DDQYYwbGNibCTgMqmVzP/uHgz8SGOwJamEAO6ytJnEDb4/BAaDDEjcwsB3Gq8PgzLNky7ltB4olzpwpOMyTJpE84zBbMn6HtacZ3njbVpfH35O++eMPGxvb/vY2Y/wOY2BgkWBgOJwA5QDZzIQ0AJUAY6EugaCyUTAKRsEoGLkAAAXbSZrUl6/cAAAAAElFTkSuQmCC","orcid":"","institution":"Korea Advanced Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Sukyung","middleName":"","lastName":"Park","suffix":""}],"badges":[],"createdAt":"2026-04-04 08:40:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9318966/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9318966/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108976709,"identity":"88461209-78ce-4fb8-8dbd-eead2b99f369","added_by":"auto","created_at":"2026-05-11 11:28:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":830689,"visible":true,"origin":"","legend":"","description":"","filename":"PersonalizedKinematicConstraintBasedDriftCorrectionfor3DGolfSwingTrajectoryMonitoringwithaWristWornIMU.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9318966/v1_covered_2d3328d3-8b98-4ea8-8195-b5c9e1b639ee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Personalized Kinematic Constraint-Based Drift Correction for 3D Golf Swing Trajectory Monitoring with a Wrist-Worn IMU","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9318966/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9318966/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Monitoring three-dimensional (3D) golf swing trajectories is important for performance analysis, yet accurate measurement still relies largely on laboratory-based motion capture systems. Wrist-worn inertial measurement units (IMU) provide a practical alternative, but trajectory estimation is limited by orientation errors and integration drift. Existing drift correction approaches typically rely on predefined kinematic constraints that are not tailored to individual swing mechanics, despite the substantial inter-individual variability observed in golf swings. In this study, we propose a subject-specific drift correction framework for 3D golf swing trajectory monitoring using a wrist-worn IMU. The relative wrist displacement between address and impact was defined as a subject-specific kinematic constraint. This constraint was estimated from monocular video by compensating for depth ambiguity using club length as a geometric constraint. A bidirectional long short-term memory (BiLSTM) model was then trained to infer the same constraint from IMU signals, allowing subsequent drift correction using IMU data alone. The proposed method reduced entire swing and downswing trajectory errors relative to motion capture from approximately 68 cm and 78 cm for uncorrected IMU integration to approximately 20 cm and 15 cm, respectively. Furthermore, a trajectory consistency metric computed from the corrected IMU-estimated trajectories showed high linear agreement with the motion capture-based reference for intentionally faster than preferred swings (R = 0.86, p \u003c 0.05), indicating the feasibility of monitoring relative changes in swing consistency with a single wrist-worn IMU. These findings suggest that a biomechanics-informed kinematic constraint, namely the within-subject consistency of address-to-impact wrist displacement, can serve as a compact learning target for IMU drift correction. By grounding personalization in a domain-specific prior rather than relying on generalized models, the framework reduces reliance on motion capture-based large scale trajectory labels and enables subject-specific calibration from a monocular camera session. 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