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 7 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 metrics were compared to identify which metrics exhibit desirable invariance properties, and so are good candidates for use as a stability metric. 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 energy-reducing properties of an exoskeleton are affected by changes in walking context. The metric results demonstrated that medial-lateral centre of pressure displacement and medial-lateral margin of stability exhibit strong invariance to changes in walking conditions. This suggests these dynamic stability 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.
Gordon, D. F. N., Henderson, G., & Vijayakumar, S. (2018). Effectively Quantifying the Performance of Lower-Limb Exoskeletons Over a Range of Walking Conditions. Frontiers in Robotics and AI, 5. https://doi.org/10.3389/frobt.2018.00061