Gait self-learning control based on reference trajectory generation online for an asymmetric limb rehabilitation exoskeleton

Abstract

Lower limb exoskeleton (LEX) are widely used to assist stoke survivors with walking dysfunction, which is lack of a more flexible trajectory and fails to address the control challenge posed by gait variability and asymmetry in rehabilitation training. This paper introduces an asymmetric self-learning lower exoskeleton (AS-LEX) based on reference trajectory generation for the affected side. Motor intent of the unaffected limb based on thresholds was identified to classify the gait phase of stance and swing. A parameterized gait trajectory was generated online, namely a combination of circular trajectory in the stance phase and an elliptical trajectory in the swing phase. Gait self-learning control is presented to make the affected limb adaptively learn the gait parameters generated by the unaffected limb. Feasibility of the AS-LEX is demonstrated experimentally using three healthy subjects. Resuls demonstrate that overground walking in a more natural speed (with a stride length 600 mm and 700 mm) make subjects more actively learn gait of the affected side from the unaffected side. Additionally, experiments of the fatigue level of the affected limb and human-robot interaction torques were carried out, and the results indicate a more natural gait and reduced interaction forces with the AS-LEX.