Gordon Daniel F N, Henderson Graham, Vijayakumar Sethu
Institute of Perception, Action, and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom.
Front Robot AI. 2018 Jun 27;5:61. doi: 10.3389/frobt.2018.00061. eCollection 2018.
Exoskeletons and other wearable robotic devices have a wide range of potential applications, including assisting patients with walking pathologies, acting as tools for rehabilitation, and enhancing the capabilities of healthy humans. However, applying these devices effectively in a real-world setting can be challenging, as the optimal design features and control commands for an exoskeleton are highly dependent on the current user, task and environment. Consequently, robust metrics and methods for quantifying exoskeleton performance are required. This work presents an analysis of walking data collected for healthy subjects walking with an active pelvis exoskeleton over three assistance scenarios and five walking contexts. Spatial and temporal, kinematic, kinetic and other novel dynamic gait metrics were compared to identify which metrics exhibit desirable invariance properties, and so are good candidates for use as a stability metric over varying walking conditions. Additionally, using a model-based approach, the average metabolic power consumption was calculated for a subset of muscles crossing the hip, knee and ankle joints, and used to analyse how the energy-reducing properties of an exoskeleton are affected by changes in walking context. The results demonstrated that medio-lateral centre of pressure displacement and medio-lateral margin of stability exhibit strong invariance to changes in walking conditions. This suggests that these dynamic gait metrics are optimised in human gait and are potentially suitable metrics for optimising in an exoskeleton control paradigm. The effectiveness of the exoskeleton at reducing human energy expenditure was observed to increase when walking on an incline, where muscles aiding in hip flexion were assisted, but decrease when walking at a slow speed. These results underline the need for adaptive control algorithms for exoskeletons if they are to be used in varied environments.
外骨骼和其他可穿戴机器人设备具有广泛的潜在应用，包括辅助患有行走障碍的患者、作为康复工具以及增强健康人的能力。然而，在现实环境中有效应用这些设备可能具有挑战性，因为外骨骼的最佳设计特征和控制指令高度依赖于当前的用户、任务和环境。因此，需要用于量化外骨骼性能的稳健指标和方法。这项工作对健康受试者在三种辅助场景和五种行走情境下穿着主动骨盆外骨骼行走时收集的步行数据进行了分析。比较了空间和时间、运动学、动力学以及其他新颖的动态步态指标，以确定哪些指标表现出理想的不变性属性，因此是在不同行走条件下用作稳定性指标的良好候选指标。此外，使用基于模型的方法，计算了跨越髋、膝和踝关节的一部分肌肉的平均代谢功率消耗，并用于分析外骨骼的节能特性如何受到行走情境变化的影响。结果表明，压力中心的中外侧位移和稳定性的中外侧边缘对行走条件的变化表现出很强的不变性。这表明这些动态步态指标在人类步态中得到了优化，并且可能是在外骨骼控制范式中进行优化的合适指标。观察到，当在斜坡上行走时，外骨骼在减少人体能量消耗方面的有效性会增加，此时有助于髋部屈曲的肌肉得到了辅助，但在低速行走时会降低。这些结果强调，如果要在外骨骼在各种环境中使用，就需要自适应控制算法。
