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

Assistive robotic devices like exoskeletons offer the promise of improving mobility for millions of people. However, developing devices that improve an objective mobility metric is challenging. Human-in-the-loop optimization is a systematic approach for personalizing robotic assistance to maximize a mobility metric that has improved device performance for different metrics and applications. Successfully performing human-in-the-loop optimization requires the experimenter to make many decisions, like selecting the appropriate optimization algorithm, hyperparameters, and convergence criteria. Typically, selecting these experimental settings involves pilot experimentation. We propose an approach that uses a probabilistic surrogate model, mapping assistance parameters to corresponding experimental evaluations of the objective mobility metric, to simulate human-in-the-loop optimization and inform these decisions. In this paper, we form a surrogate model of the metabolic landscape of walking with exoskeleton assistance using an existing experimental dataset. We simulate human-in-the-loop optimization by using a synthetic metabolic landscape model to evaluate the metabolic cost of walking with different assistance parameters, instead of performing an experimental measurement. We perform three simulated scenarios optimizing assistance for an expert subject, a novice subject adapting to the device, and an expert subject with up to 20 assistance parameters. The code and analyses from this work are open-source to promote use by other researchers. Simulation enables direct comparison of optimization settings to inform experimental human-in-the-loop optimization and potentially reduce the resources and time required to develop effective assistive devices.