1. Ground Perturbation Detection via Lower-Limb Kinematic States During Locomotion
- Author
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Tagliaferri, Maria T., Campeggi, Leonardo, Beck, Owen N., and Kang, Inseung
- Subjects
Computer Science - Robotics - Abstract
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 further optimized our detector with an open-source ground perturbation biomechanics dataset. A pilot experimental validation with five able-bodied subjects demonstrated that our model detected ground perturbations with 97.8% accuracy and only a delay of 23.1% within the gait cycle, outperforming the benchmark by 46.8% 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., Comment: 6 pages, 6 figures
- Published
- 2024