Human gait events fast recognition method via surface electromyography

Abstract

Real-time and accuracy detection of human motion during active training stage were required for lower limb rehabilitation robot. A dynamic surface EMG based human gait events fast recognition method was proposed. Firstly, the surface EMG generation model was established and the skeletal muscle activity during gait was analyzed, the gait event perception method with surface EMG intensity and its variation was put forward. Then, an ANFIS model was built to recognize the supporting phase and swing phase, which used the dynamic surface EMG signals of vastus lateralis lie in the both of left and right thigh as the signal source. The experimental results show that the average correct rate may reach 95.3% compared with results detected from force plate, the average time errors for heel strike (HS) and toe off (TO) timing detection are 21.4 ms and 24.5 ms respectively. Moreover, the method proposed also shows a strong robustness against the surface EMG difference between gaits.