Metabolic cost of walking with electromechanical ankle exoskeletons under proportional myoelectric control on a treadmill and outdoors

Abstract

Lower limb robotic exoskeletons are often studied in the context of steady state treadmill walking in a laboratory environment. However, the end goal for exoskeletons is to be used in real world, complex environments. To reach the point that exoskeletons are openly adopted into our everyday lives, we need to understand how the human and robot interact outside of a laboratory. Metabolic cost is often viewed as a gold standard metric for measuring exoskeleton performance but is rarely used to evaluate performance at non steady state walking outside of a laboratory. In this study, we tested the effects of robotic ankle exoskeletons under proportional myoelectric control on the cost of transport of walking both inside on a treadmill and outside overground. We hypothesized that walking with the exoskeletons would lead to a lower cost of transport compared to walking without them both on a treadmill and outside. We saw no significant increases or decreases in cost of transport or exoskeleton mechanics when walking with the exoskeletons compared to walking without them both on a treadmill and outside. We saw a strong negative correlation between walking speed and cost of transport when walking with and without the exoskeletons. In the future, research should consider how performing more difficult tasks, such as incline and loaded walking, affects the cost of transport while walking with and without robotic ankle exoskeletons. The value of this study to the literature is that it emphasizes the importance of both hardware dynamics and controller design towards reducing metabolic cost of transport with robotic ankle exoskeletons. When comparing our results to other studies using the same hardware with different controllers or very similar controllers with different hardware, there are a wide range of outcomes as to metabolic benefit.

Gait variability of outdoor vs treadmill walking with bilateral robotic ankle exoskeletons under proportional myoelectric control

“…For each participant we calculated the coefficient of variation (CoV) of stride time, stride length, stance time, and swing time by dividing the standard deviation by the mean and multiplying by 100 to present the data as a percentage. Previous publications focus on the metabolic and neuromechanical adaptations to this controller and exoskeleton combination [ 44 , 45 ], so we do not go into detail on the findings. We calculated EMG root mean square (RMS) values during stance for four muscles of each leg (tibialis anterior, medial gastrocnemius, lateral gastrocnemius, and soleus) for each condition and day.…”

Simulating human-in-the-loop optimization of exoskeleton assistance to compare optimization algorithm performance

“…Mobility challenges impact the lives of 1 in 7 adults, with major repercussions on physical health, mental health, independence, and quality of life [1]. Assistive robotic devices like exoskeletons offer a potential solution to mobility challenges but most exoskeletons do not yet provide benefits in real-world settings [2], [3]. Iterative exoskeleton controller development has led to steady improvements in device performance evaluated in laboratory settings [4].…”