A Gait Rehabilitation Strategy Inspired by an Iterative Learning Algorithm

Abstract

Robotic gait rehabilitation devices enable more efficient and convenient gait rehabilitation by mimicking the functions of physical therapists. In manual gait rehabilitation training, physical therapists try for patients to practice and memorize normal gait patterns by applying assistive torque to the patient's joint if the patient's gait deviates from the normal gait. Thus, it is one of the most important factors in the robotic gait rehabilitation devices to determine the amount of assistive torque to practice the normal gait. In this paper, the gait rehabilitation strategy inspired by an iterative learning algorithm is proposed which uses the cyclic and repetitive characteristic of gait motions. In the proposed strategy, the amount of assistive torque in the current stride is calculated based on the information in the previous stride. The simulation results with human models, and experimental results using an active knee orthosis are presented, which verify that the proposed strategy can generate appropriate torque to practice the knee motions for the normal gait.