A Benchmark Dataset for Lower-Limb Exoskeletons Assisting Five Ambulation Modes

A Benchmark Dataset for Lower-Limb Exoskeletons Assisting Five Ambulation Modes | 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 A Benchmark Dataset for Lower-Limb Exoskeletons Assisting Five Ambulation Modes Clément Lhoste, Alberto Cantón, Emek Barış Küçüktabak, Matthew R. Short, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6123772/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Mar, 2026 Read the published version in Journal of NeuroEngineering and Rehabilitation → Version 1 posted 12 You are reading this latest preprint version Abstract Background: Lower-limb exoskeletons are a useful tool in rehabilitation settings as they can provide customized assistance to individuals during functional exercises. These approaches typically rely on state-machine-based control with impedance controllers tailored to different locomotion phases, ensuring appropriate assistance across various activities and environments. However, these methods necessitate lengthy calibration procedures, as many impedance parameters need to be fine-tuned to provide appropriate assistance for various activities (e.g. overground walking, ramps, and stairs). Methods: The purpose of this study is three-fold. First, we present a statemachine- based control strategy for partial assistance lower-limb exoskeletons. Second, we present a computational method to extract reference trajectories from a benchmark dataset [1], enabling the identification of state-machine controller parameters and simplifying calibration procedures. Third, we provide a dataset of 19 healthy individuals walking in five walking conditions (overground walking, upstairs, downstairs, up ramps, and down ramps) using either the state-machine approach or a transparent controller. Results: The analysis of the proposed controller showed a statistically significant reduction in interaction power with the state-machine controller across most of the ambulation modes (p < 0.001), indicating greater user assistance. Preferred walking speed was notably faster with the state-machine controller, particularly on level ground, ramps and stairs ascent (25-32% increase). Kinematic analysis revealed closer alignment to able-bodied gait patterns with the state-machine controller, suggesting improved gait quality. At the same time, the dataset of the collected locomotion activities (dataset link) will constitute a new benchmark dataset for locomotion. Conclusions: In this work, we presented and evaluated a novel state-machinebased control strategy for partial-assistance lower-limb exoskeletons. In this approach, reference trajectories are extracted from a benchmark dataset, simplifying calibration procedures. Additionally, we provide a dataset of 19 healthy individuals using two exoskeleton controllers. The proposed controller will be applied to patient populations, while the dataset will serve as a valuable resource for advancing robust and effective control mechanisms through machine learning techniques. Exoskeleton Rehabilitation Control Full Text Additional Declarations No competing interests reported. Supplementary Files Lhoste2025Benchmark.mp4 Cite Share Download PDF Status: Published Journal Publication published 20 Mar, 2026 Read the published version in Journal of NeuroEngineering and Rehabilitation → Version 1 posted Editorial decision: Revision requested 04 Aug, 2025 Reviews received at journal 05 Jun, 2025 Reviewers agreed at journal 14 May, 2025 Reviews received at journal 19 Mar, 2025 Reviewers agreed at journal 13 Mar, 2025 Reviewers agreed at journal 13 Mar, 2025 Reviewers agreed at journal 10 Mar, 2025 Reviewers agreed at journal 10 Mar, 2025 Reviewers invited by journal 08 Mar, 2025 Editor assigned by journal 28 Feb, 2025 Submission checks completed at journal 28 Feb, 2025 First submitted to journal 27 Feb, 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. 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At the same time, the dataset of the collected locomotion activities (dataset link) will constitute a new benchmark dataset for locomotion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e In this work, we presented and evaluated a novel state-machinebased control strategy for partial-assistance lower-limb exoskeletons. In this approach, reference trajectories are extracted from a benchmark dataset, simplifying calibration procedures. Additionally, we provide a dataset of 19 healthy individuals using two exoskeleton controllers. 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