Ground Perturbation Detection via Lower-Limb Kinematic States During Locomotion

2024-12-09

arXiv.org

AI Summary

Existing whole-body angular momentum–based disturbance detection methods for lower-limb exoskeletons used by older adults suffer from high computational latency, hindering real-time fall prevention in response to ground disturbances. Method: This study proposes a lightweight, exoskeleton-integrated disturbance detection paradigm that relies solely on kinematic states—specifically, lower-limb joint angles and angular velocities. It models deviations from steady-state gait trajectories and employs supervised learning classification, trained on an open-source biomechanical disturbance dataset, to enable early disturbance identification without computationally intensive whole-body dynamics. Contribution/Results: The method significantly improves real-time performance and robustness. In experiments with five healthy subjects, it achieves 98.8% disturbance classification accuracy with a detection delay of only 23.1% of the gait cycle—reducing latency by 47.7 percentage points versus the baseline method—thereby providing reliable, low-latency input for exoskeleton feedforward control.

Technology Category

• Intelligent Robots: State Estimation
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Detect ground perturbations during locomotion using lower-limb kinematics
• Improve exoskeleton response time by reducing computational delays
• Enhance fall prevention accuracy in robotic assistive exoskeletons

Innovation

• Uses lower-limb kinematic states for detection
• Tracks deviations from steady-state trajectories
• Optimized with data-driven biomechanics dataset