Experiment-free exoskeleton assistance via learning in simulation

Abstract Our experimental protocol details the human subject evaluation of a novel, simulation-based approach for optimizing exoskeleton control strategies. Utilizing multi-layer perceptron neural networks, this approach achieves: modeling human kinematics, optimizing muscle coordination, and automating exoskeleton assistance. This method capitalizes on the strengths of data-driven machine learning to refine control strategies, significantly reducing metabolic costs across various activities including walking, running, and stair climbing. This experiment protocol aims to collect metabolic rate and kinematic data during 3 activities (walking, running, stair climbing) in 3 conditions (No Exo, Assist On, Assist Off) to demonstrate the controller’s ability to generate smooth and synergistic assistance to different locomotion activities. The entire protocol can be completed in approximately 30 minutes for each activity of each subject (n=8).