Biomechanical Optimization and Reinforcement Learning Provide Insights into Ankle-to-Hip Strategy Transitions in Human Postural Control

Biomechanical Optimization and Reinforcement Learning Provide Insights into Ankle-to-Hip Strategy Transitions in Human Postural Control | 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 Optimization and Reinforcement Learning Provide Insights into Ankle-to-Hip Strategy Transitions in Human Postural Control Seongwoong Hong, Sukyung Park This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5151206/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Human postural control strategies, categorized as ankle or hip strategies, adapt to varying perturbation magnitudes and support surface sizes. While numerous studies have characterized these strategies, few have explored the underlying mechanisms driving the transition from ankle to hip strategy. This study investigated whether postural strategy transitions can be explained through an optimization mechanism incorporating biomechanical constraints. We analyzed postural strategy changes in human responses to backward perturbations and developed a reinforcement learning (RL)-based optimization model. The biomechanical constraint was defined as the center of pressure (CoP) range limitation to the metatarsal joint. The control objective function featured a novel CoP constraint penalty, complemented by terms for upright posture recovery and control effort minimization. The RL-based optimization model successfully reproduced the ankle-to-hip strategy transition observed in human postural responses. With increasing perturbation magnitude, the model demonstrated a pattern of limited ankle torque coupled with increased hip joint kinematics, closely aligning with observed human postural adaptations. These results suggest that the adaptive nature of human postural strategy transitions can be understood within an optimization framework incorporating biomechanical constraints. Additionally, this study supports the use of RL models, capable of implementing nonlinear optimization, as a valuable tool for comprehensively analyzing diverse adaptive characteristics in human movement. Physical sciences/Engineering/Biomedical engineering Physical sciences/Engineering/Mechanical engineering Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Feb, 2025 Reviews received at journal 15 Feb, 2025 Reviewers agreed at journal 05 Feb, 2025 Reviews received at journal 06 Nov, 2024 Reviewers agreed at journal 29 Oct, 2024 Reviewers invited by journal 27 Oct, 2024 Editor assigned by journal 17 Oct, 2024 Editor invited by journal 14 Oct, 2024 Submission checks completed at journal 11 Oct, 2024 First submitted to journal 25 Sep, 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. 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