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 This study presents three contributions: (1) a state-machine-based control strategy for partial assistance lower-limb exoskeletons, (2) a computational method to extract reference trajectories from a benchmark dataset (Camargo et al. in J Biomech 119:110320, 2021), enabling the identification of state-machine controller parameters and simplifying calibration procedures and (3) 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 state-machine controller produced in average more negative interaction power (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(-2.6\times 10^{-2}\)</EquationSource> </InlineEquation> W/kg) compared to transparent control (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(0.8\times 10^{-2}\)</EquationSource> </InlineEquation> W/kg), 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 (<a href="http://datadryad.org/stash/share/X8hZMSDHYq3oXA1AB-Zp6ar_726sqAhyCBWxBxbrIJs">dataset link</a>) will constitute a new benchmark dataset for locomotion. Conclusions In this work, we presented and evaluated a novel state-machine-based 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.

Simplifying rehabilitation control of lower-limb exoskeletons in five ambulation modes via dataset-driven state-machine calibration

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发表日期：

2026年3月20日

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Clément Lhoste,
Alberto Cantón,
Emek Barış Küçüktabak,
Matthew R. Short,
Rebecca Schwanemann,
Shoshana Clark,
Daniel Ludvig,
Kevin Lynch,
Levi Hargrove,
Jesús Tornero,
Lorenzo Vianello,
José Pons

摘要

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

This study presents three contributions: (1) a state-machine-based control strategy for partial assistance lower-limb exoskeletons, (2) a computational method to extract reference trajectories from a benchmark dataset (Camargo et al. in J Biomech 119:110320, 2021), enabling the identification of state-machine controller parameters and simplifying calibration procedures and (3) 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 state-machine controller produced in average more negative interaction power ( \(-2.6\times 10^{-2}\) W/kg) compared to transparent control ( \(0.8\times 10^{-2}\) W/kg), 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-machine-based 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.