Simultaneous Gait Event Intention Detection Using Single sEMG Sensor for Lower Limb Exoskeleton

Accurate detection of gait event intention and sending it to lower limb exoskeleton (LLE) is the key to achieve active rehabilitation. Most existing surface electromyography (sEMG)-based gait event intention detection methods suffer from insufficient generalization and complex detection. In this paper, we propose a novel approach for detecting gait event intention using a single sEMG sensor. The gait event intention is obtained by detecting the peak activity of the rectus femoris during the stance period. First, the root mean square (RMS) features are extracted from the sEMG data of the rectus femoris. Then, the data groups composed of the RMS features are smoothed and all extreme points are calculated. Finally, the midstance (MSt) events are discovered when the latest maximum point satisfies the preset condition. The experimental results of three different gait speeds showed that the proposed approach could adapt to different walking speeds and maintain a high detection accuracy of gait event intention detection. This study provides a convenient and reliable detection approach for gait research of LLE.