Individual Gait Generation for Rehabilitation Robots Based on GLS network

Abstract

In recent years, there has been an increase in the number of patients with motor dysfunction due to neurological disorders such as stroke and spinal cord injury. Existing rehabilitation medical resources are unable to provide one-on-one rehabilitation treatment for patients. Lower limb rehabilitation robots (LLRRs) are used as assistants to rehabilitation physicians to help patients with rehabilitation training by simulating the walking gait of a normal person. Each patient’s individual degree of impairment varies from person to person, and the parameters of the lower extremity differ, which leads to different rehabilitation training programs used by the patient. Therefore, how to tailor the reference trajectory for patients according to their physical and gait parameters becomes the focus of research at this stage. In this paper, a gait generation model based on GLS network is designed to provide continuous personalized gait trajectories for patients. The GLS network is composed of an LSTM (Long Short-Term Memory) network based on the gap loss function and a sparrow search algorithm (SSA).The gap loss is presented based on the difference between the initial angle and the end joint angle of each cycle. The experimental results are analyzed by mean absolute deviation (MAD). The experimental results show that the gait trajectory generated by the GLS network reduces the experimental error by 16.9 % compared to the trajectory generated by the conventional algorithm (PSO). Therefore, our proposed GLS network-based generation of personalized continuous gait trajectories generates gait trajectories that are more similar to the actual gait trajectories.