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

Abstract

Several powered exoskeletons have been developed and commercialized to assist people with complete spinal cord injury. For motion control of a powered exoskeleton, a normal gait pattern is often applied as a reference. However, the physical ability of paraplegics and the degrees of freedom of powered exoskeletons are totally different from those of people without disabilities. Therefore, this paper introduces a novel gait pattern depart from the normal gait, which is proper to the paraplegics. Since a human is included, the system of the powered exoskeleton has lots of motion uncertainties that may not be perfectly predicted resulting from different physical properties of paraplegics (SCI level, muscular strength of the upper body, body parameters, inertia), actions from crutches (position and timing to put), several types of training (period, methodology), etc. Then, to find a stable and safe gait pattern adapted to the individual user, an iterative way to compensate the gait pattern is also required. In this paper, human iterative learning algorithm, which utilizes the accumulated data during walking to adjust the gait trajectories is proposed. Additionally, the effectiveness of the proposed gait pattern is verified by human walking experiments.