Ground Perturbation Detection via Lower-Limb Kinematic States During Locomotion

Falls during daily ambulation activities are a leading cause of injury in older adults due to delayed physiological responses to disturbances of balance. Lower-limb exoskeletons have the potential to mitigate fall incidents by detecting and reacting to perturbations before the user. Although commonly used, the standard metric for perturbation detection, whole-body angular momentum, is poorly suited for exoskeleton applications due to computational delays and additional tunings. To address this, we developed a novel ground perturbation detector using lower-limb kinematic states during locomotion. To identify perturbations, we tracked deviations in the kinematic states from their nominal steady-state trajectories. Using a data-driven approach, we optimized our detector with an open-source ground perturbation biomechanics dataset. A nine-subject cross-validation demonstrated that our model distinguished perturbed from unperturbed gait cycles with 95.5% accuracy and only a delay of 33.1% within the gait cycle, outperforming the benchmark by 49.4% in detection accuracy. The results of our study offer exciting promise for our detector and its potential utility to enhance the controllability of robotic assistive exoskeletons.