用下肢运动状态检测地面扰动，提升外骨骼实时防跌能力
Ground Perturbation Detection via Lower-Limb Kinematic States During Locomotion
- 基于下肢运动轨迹偏差检测地面扰动
- 98.8%准确率，延迟仅占步态周期23.1%
- 适合外骨骼实时控制，比现有方法提升47.7%精度
老年人日常行走时因平衡扰动反应迟缓导致跌倒频发。下肢外骨骼可通过提前检测并响应扰动来降低跌倒风险。然而，传统基于全身角动量的检测方法存在计算延迟大、参数调优复杂等问题，不适用于外骨骼场景。为此，本文提出一种基于行走过程中下肢运动状态的新扰动检测方法：通过追踪运动状态偏离稳态轨迹的程度来识别扰动。利用开源地面扰动生物力学数据集进行数据驱动优化。五名健康受试者的初步实验验证表明，该模型在步态周期内仅延迟23.1%的情况下实现98.8%的扰动/正常步态区分准确率，相比基准方法检测精度提升47.7%。研究结果为提升机器人辅助外骨骼的控制能力提供了良好前景。
原文摘要 · Abstract (English)
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.
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