Ground Perturbation Detection via Lower-Limb Kinematic States During Locomotion

TL;DR

This paper introduces a novel ground perturbation detection method using lower-limb kinematic states during walking, significantly improving detection accuracy and speed over existing metrics, with potential to enhance exoskeleton control.

Contribution

A new perturbation detector based on lower-limb kinematic deviations, optimized with biomechanics data, offering faster and more accurate detection for exoskeleton applications.

Findings

Achieved 98.8% detection accuracy in pilot tests.

Detected perturbations with only 23.1% delay within gait cycle.

Outperformed benchmark method by 47.7% in accuracy.

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 distinguished perturbed from unperturbed gait cycles with 98.8% accuracy and only a delay of 23.1% within the gait cycle, outperforming the benchmark by 47.7% 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.