Online Gait Generation for an Exoskeleton Used in Lower Limb Rehabilitation

Abstract

Providing walking assistance for mild hemiplegia patients with lower limb rehabilitation exoskeleton has gained considerable attention. The patients' motion characteristics should be emphasized, meanwhile the planning gait should always maintain the balance ability in the walking procedure. In this paper, an online balance gait generation strategy is proposed for an exoskeleton used in lower limb rehabilitation. In the present research, the online gait generation strategy is explained and consists of a gait planning and a gait control strategy. In the gait planning strategy, the healthy swing foot trajectory has been learned, modelled and modified for next cycles' exoskeleton motion by applying improved differential evolution Adaptive Hopf (iDE AHopf) oscillators. Meanwhile to illustrate the human motion intention, the dynamic step length estimation method is utilized to estimate the step length. The discrete step planner determines the motion of the exoskeleton's centre of gravity (CoG). In the gait control strategy, the online balance gait regulation could regulate the exoskeleton joint reference adaptively to confirm the maximum zero-moment-point (ZMP) walking stability according to the PID controller feedback joint signal. Finally, the corresponding simulation results demonstrate the effectiveness of the proposed strategy by verifying the gait learning rate and ZMP stability margin.

Individualized Gait Trajectory Prediction Based on Fusion LSTM Networks for Robotic Rehabilitation Training

“…Robot-assisted gait training for hemiparetic stroke patients, has become a research hotspot [2,3]. Most of the lower limb exoskeleton robots carry out rehabilitation training based on gait tracking.…”