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

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). 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. The state-machine controller produced in average more negative interaction power (-2. 6 10^-2 W/kg) compared to transparent control (0. 8 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. 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.