sEMG-signal and IMU sensor-based gait sub-phase detection and prediction using a user-adaptive classifier.

This paper presents a gait sub-phase detection and prediction approach using surface electromyogram (sEMG) signals, pressure sensors, and the knee angle for a lower-limb power-assist robot. Pattern recognition and machine learning models using sEMG signals have several inherent problems for gait sub-phase detection. These problems are due to recognition delay, lack of consideration for the unique characteristics of sEMG signals based on the subject, and meaningless features. To solve these problems, we propose a new labeling technique based on the heel and toe, a muscle and feature selection, a user-adaptive classifier using a weighted voting technique to achieve gait sub-phase detection, and a gait sub-phase prediction technique using interpolation. Experimental results show that the average accuracies of the proposed labeling, the muscle and feature selection, and the user-adaptive classifier using weighted voting are 7%, 12%, and 17% better, respectively, than the existing methods using physical sensors. Results also show that the average prediction time of the proposed method is 80% faster than the existing methods.