Adaptive control of a robotic exoskeleton for neurorehabilitation

Abstract

Neurorehabilitation efficiency increases with therapy intensity and subject's involvement during physical exercises. Robotic exoskeletons could bring both features, if they could adapt the level of assistance to patient's motor capacities. To this aim, we developed an exoskeleton controller, based on adaptive techniques, that can actively modulate the stiffness of the robotic device in function of the subject's activity. We tested this control law on one healthy subject with an upper-limb exoskeleton. The experiment consisted in learning a trajectory imposed by the robot. The early results show the different features allowed by our controller with respect to controllers commonly used for neurorehabilitation with exoskeletons.