Optimized hip-knee-ankle exoskeleton assistance reduces the metabolic cost of walking with worn loads

Abstract

Background Load carriage is common in a wide range of professions, but prolonged load carriage is associated with increased fatigue and overuse injuries. Exoskeletons could improve the quality of life of these professionals by reducing metabolic cost to combat fatigue and reducing muscle activity to prevent injuries. Current exoskeletons have reduced the metabolic cost of loaded walking by up to 22% relative to walking in the device with no assistance when assisting one or two joints. Greater metabolic reductions may be possible with optimized assistance of the entire leg. Methods We used human-in the-loop optimization to optimize hip-knee-ankle exoskeleton assistance with no additional load, a light load (15% of body weight), and a heavy load (30% of body weight) for three participants. All loads were applied through a weight vest with an attached waist belt. We measured metabolic cost, exoskeleton assistance, kinematics, and muscle activity. We performed Friedman’s tests to analyze trends across worn loads and paired t-tests to determine whether changes from the unassisted conditions to the assisted conditions were significant. Results Exoskeleton assistance reduced the metabolic cost of walking relative to walking in the device without assistance for all tested conditions. Exoskeleton assistance reduced the metabolic cost of walking by 48% with no load (p = 0.05), 41% with the light load (p = 0.01), and 43% with the heavy load (p = 0.04). The smaller metabolic reduction with the light load may be due to insufficient participant training or lack of optimizer convergence. The total applied positive power was similar for all tested conditions, and the positive knee power decreased slightly as load increased. Optimized torque timing parameters were consistent across participants and load conditions while optimized magnitude parameters varied. Conclusions Whole-leg exoskeleton assistance can reduce the metabolic cost of walking while carrying a range of loads. The consistent optimized timing parameters across participants and conditions suggest that metabolic cost reductions are sensitive to torque timing. The variable torque magnitude parameters could imply that torque magnitude should be customized to the individual, or that there is a range of useful torque magnitudes. Future work should test whether applying the load to the exoskeleton rather than the person’s torso results in larger benefits.

Shared Autonomy Locomotion Synthesis with a Virtual Powered Prosthetic Ankle

“…Identified control parameters may provide starting points during fine tuning with real life users, which may avoid control strategies that do not fully take part in the shared autonomy of the movement they are supposed to assist. Although DRL was used to learn the device policy as well, the DRL locomotion synthesizer could be combined with other, non deep learning based controllers [46].…”

A review of the design of load-carrying exoskeletons

“…However, the exoskeleton assisting the ankle would increase the distal inertia inevitably, and a load acting far from the centre of mass would significantly increase the metabolic consumption [53]. To avoid this disadvantage, exoskeletons assisting the knee joint [54][55][56] or the hip joint [12,[57][58][59] was developed which are shown in Figure 2(c) and (d).…”

“…Due to the lack of external power, passive devices cannot provide sufficient power and the performance in reducing muscle activity and metabolic cost is compromised compared with the active ones. For example, in a study [74] of a passive exoskeleton designed to carry loads, the average median oxygen consumption decreased by only 9.45% in an experiment where a mass of 15 kg was lifted 10 times to a height of 1.5 m. Table 2 gives an overview of lower limb exoskeleton providing joint torques addressing the metabolic reduction performance [10,[12][13][14]42,53,59,63,64,68,70,74,75].…”

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

“…Human-in-the-loop optimization does not use an explicit model of the human or robotic system, relying instead on the experimental evaluations taken directly from the human-robot system. Human-in-the-loop optimization has enabled substantial performance improvements for exoskeleton devices evaluated in a laboratory setting [7], [8], [9], [10], [11] and the real world [12].…”