Design of a control framework for lower limb exoskeleton rehabilitation robot based on predictive assessment

Abstract

BackgroundPatients suffering from lower limb dyskinesia, especially in early stages of rehabilitation, have weak residual muscle strength in affected limb and require passive training by the lower limb rehabilitation robot. Anatomy indicates that the biceps femoris short head muscle has a strong influence on knee motion at the swing phase of walking. We sought to explore how it would influence on gait cycle in optimization framework. However, the training trajectory of conventional rehabilitation robots performing passive training usually follows gait planning based on general human gait data, which cannot simultaneously ensure both effective rehabilitation of affected limbs with varying severity pathological gait and comfort of the wearer within a safe motion trajectory. MethodsTo elucidate the effects of weakness and contracture, we systematically introduced isolated defects into the musculoskeletal model and generated walking simulations to predict the gait adaptation due to these defects. An impedance control model of the rehabilitation robot is developed. Knee joint parameters optimized by predictive forward dynamics simulation are adopted as the expected values for the robot controller to achieve customized adjustment of the robot motion trajectory. FindingsSevere muscle contracture leads to severe knee flexion; severe muscle weakness induces a significant posterior tilt of the upper trunk, which hinders walking speed. InterpretationOur simulation results attempt to reveal pathological gait features, which may help to reproduce the simulation of pathological gait. Furthermore, the robot simulation results show that the robot system achieves a speedy tracking by setting a larger stiffness value. The model also allows the implementation of different levels of damping or elasticity effects.Trial registration: The method proposed in this paper is an initial basic study that did not reach clinical trials and therefore retains retrospectively registered.