EMG-Based Gait Speed Control of a Lower-limb Exoskeleton for Gait Rehabilitation: A Proof-of-Concept Study with Post-stroke Chronic Hemiparetic Patients

The purpose of this paper is to introduce an EMG-based real-time gait speed control algorithm embedded robot-assisted gait training (RAGT) system for gait rehabilitation of post-stroke hemiparetic patients and to verify its feasibility and safety. According to the previous researches, the gait speed of ongoing step is linearly proportional to the maximum value of Soleus electromyogram (EMG) waveform length (WL) in the prior gait cycle. This tendency was observed not only on healthy people or the nonparetic side leg of post-stroke hemiparetic patients, but also on the affected side leg. Therefore, we developed an algorithm that can control the gait speed according to the magnitude of Soleus EMG signal and embeded it to a lower-limb exoskeleton to design task-oriented RAGT to improve effectiveness of gait rehabilitation for hemipatic patients in subacute phase. Firstly, we applied it to post-stroke patients in chronic phase. A total of 30 patients were participated and divided into the following three groups: constant gait speed RAGT Group A, EMG-based gait speed-controlled RAGT Group B, and traditional gait training Group C. All subjects safely completed a total of 10 days of gait training protocol, approximately 30 min/day, 2-3 days apart. Gait performance was measured before and after the training. The result showed the EMG WL in Group B was significantly increased. In this study, we proposed a novel RAGT system with EMG-based gait speed feedback and verified. The results confirmed its applicability with safety and in future clinical research with patients who require gait rehabilitation after stroke.