Simulating surfing with optimal control: Sensorfusion for biomechanical analysis

Simulating surfing with optimal control: Sensorfusion for biomechanical analysis | 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 Simulating surfing with optimal control: Sensorfusion for biomechanical analysis Alexander Weiss, Èric Lluch, Ilias Masmoudi, Michael Döllinger, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5618608/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Apr, 2025 Read the published version in Multibody System Dynamics → Version 1 posted 9 You are reading this latest preprint version Abstract Currently, there is no established biomechanical model for surfing. Especially, musculoskeletal simulations can provide valuable insights for athletes to enhance performance and prevent injuries using quantitative data such as joint angles, joint moments, and muscle forces. The dynamic nature of surfing makes it difficult to assess biomechanics in a laboratory setting. Although inertial measurement units (IMUs) offer a potential solution, relying solely on IMU data in three-dimensional musculoskeletal models presents challenges. We hypothesize a multi-modal approach combining IMU data with deep learning-based human pose estimation to simulate surfing movements. We collected data from seven experienced surfers on a river wave, providing a semi-controlled environment. We placed IMUs on the participants’ bodies, and positioned cameras around the1wave. We defined an optimal control problem to drive three-dimensional biomechanical simulations. In addition, we used an adapted contact model to represent the interaction between feet, surfboard, and water surface. Consequently, we compared the kinematics and kinetics of the front and rear legs, and analyzed muscle forces, to demonstrate the potential of our approach. The study successfully minimized tracking errors and established the first biomechanical model for surfing. Differences between rear and front leg joint moments and angles of up to 47% were found, with muscle forces being higher in the front leg’s muscles, suggesting that the weight distribution in surfing comes mainly from the front leg muscles, while the higher rear leg moments come from stabilization. This showcases the high potential for further biomechanical analysis in surfing performance analysis using our approach. Surfing Biomechanical modeling Optimal control Inertial measurement unit Human pose estimation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 Apr, 2025 Read the published version in Multibody System Dynamics → Version 1 posted Editorial decision: Revision requested 27 Feb, 2025 Reviews received at journal 24 Feb, 2025 Reviews received at journal 19 Feb, 2025 Reviewers agreed at journal 03 Feb, 2025 Reviewers agreed at journal 02 Feb, 2025 Reviewers invited by journal 16 Dec, 2024 Editor assigned by journal 12 Dec, 2024 Submission checks completed at journal 11 Dec, 2024 First submitted to journal 10 Dec, 2024 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-5618608","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":390951196,"identity":"5e653cae-cf54-4430-99bb-d73c585cb892","order_by":0,"name":"Alexander 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