Adaptive Gait Pattern Generation of a Powered Exoskeleton by Iterative Learning of Human Behavior

Abstract

Powered exoskeletons for people with complete paraplegia have been controlled based on pre-defined joint reference trajectories. As the target users of such robots may not realize any voluntary movement, the human body is fully constrained and follows the movement of the powered exoskeletal joints. The pre-defined gait pattern, however, may or may not be adequate for every user, because the gait pattern is resulting from complex interactions between the body segments and environment, as well as dynamic characteristics of the body segments. As all the persons and their body segments have different dynamic characteristics, therefore, bespoke adaptation of gait parameters is necessary in order to realize the natural gait motion, which is optimal for each user. In this paper, a baseline gait pattern parametrized with the trunk inclination angle of the user is proposed. Taking the trunk inclination angle into account, the gait pattern is expected to reflect the unique characteristics of the paraplegic walking. Moreover, the gait pattern is adjusted based on the ground contact timing, which is directly related to the adequacy of the gait pattern for the user wearing a robot. The proposed method iteratively calculates an appropriate trunk inclination angle from the information of ground contact timing. In this paper, the derivation of the proposed method and its experimental verification with WalkON Suit, a powered exoskeleton, are introduced.