Using Bayesian Optimization to Identify Optimal Exoskeleton Parameters Targeting Propulsion Mechanics: A Simulation Study

Abstract

The long-term goal of this research is to develop methods for training propulsion during walking using robotic exoskeletons that customize their intervention based on the response of an individual.In this study, we first determined the feasibility of modeling the relationship between propulsion mechanics and parameters of a robotic intervention applied at the hip and knee joints as a Gaussian process. Specifically, we used data obtained in a previous experiment that used pulses of torque applied at the hip and knee joint, at early and late stance, to establish the relationship between a 4D control parameter space and the resulting changes in hip extension and propulsive impulse at multiple strides following intervention. We estimated Gaussian models both at the group level and for each subject. Moreover, we used the estimated subject-specific models to simulate virtual human-in-the-loop optimization (HIL) experiments based on Bayesian optimization to establish the optimal settings of acquisition function and seed point selection methods.The estimated subject-specific optimal conditions have large between-subject variability in the kinetic component of propulsion mechanics (propulsive impulse), with only 31% of subjects featuring a subject-specific optimal point in the surroundings (within a sphere of radius 20% of each dimension’s range) of the group-level optimal point. Instead, variability of the effects on the kinematic component of propulsion (leg extension) were smaller (75% of the subjects within the surroundings of the group-optimal point). Virtual HIL experiments indicate that expected improvement is the most effective acquisition method, while no significant effect of seed point selection method was observed. Our study suggests that individualized training may be necessary for inducing desired effects in propulsive force generation during walking.