THE DEVELOPMENT OF AN EMG CONTROLLER-BASED ROBOTIC GAIT TRAINING SYSTEM AND ITS CLINICAL FEASIBILITY FOR SUBACUTE STROKE PATIENTS IN IMPROVING LOCOMOTIVE FUNCTION

Abstract

One-third of stroke survivors fail to regain independent ambulation and strokes have been identified as a significant source of long-term disability and a tremendous health burden. Robot-assisted gait rehabilitation is gaining traction and advocators for its inclusion as part of the routine post-stroke rehabilitation program are on the increase. However, despite the recent technological advances in the development and design of better robotics, the research evidence on the best model of robotic training remains sparse and unclear. It is therefore the aim of the current study to comparatively investigate the clinical feasibility and efficacy of a recently developed HIWIN Robotic Gait Training System (MRG-P100) combined with the use of a lab-developed MBS-E100 EMG system as a controller on facilitating the development of an appropriate gait pattern for motor impaired subacute stroke patients. The results indicated that due to the heterogeneity of stroke-induced changes in muscle characteristics, an “auto-fit” algorithm was required to allow constant monitoring and updating of the appropriate threshold based on EMG signals captured during previous gait cycle in order to determine the desired muscle activation threshold for the current gait cycle. Eighteen participants were tested using the new auto-fit algorithm and results demonstrated a significantly more fluent and physiologically appropriate gait pattern.