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.

翻译：日常行走活动中的跌倒是老年人受伤的主要原因，这源于生理系统对平衡扰动的响应延迟。下肢外骨骼有望通过在使用者察觉前检测并响应扰动来减少跌倒事件。尽管全身角动量作为常用的扰动检测标准指标，但由于计算延迟和额外调参需求，其并不适用于外骨骼应用。为此，我们开发了一种利用步态过程中下肢运动学状态的新型地面扰动检测器。为识别扰动，我们追踪了运动学状态相对于其标称稳态轨迹的偏离。采用数据驱动方法，我们进一步借助开源地面扰动生物力学数据集优化了检测器。对五名健康受试者开展的初步实验验证表明，该模型检测地面扰动的准确率达97.8%，且仅产生步态周期内23.1%的延迟，检测精度较基准方法提升46.8%。本研究结果为该检测器及其在增强机器人辅助外骨骼可控性方面的潜在应用提供了令人振奋的前景。