Joint angle prediction from EMG signals for lower limb exoskeleton

Abstract

This paper presents an algorithm for estimation of the intended knee joint angle from sEMG signals acquired from four muscles of upper limb. The algorithm was evaluated with experiments showing the calculated intended motion while performing a simple daily life activity of sitting in a squat position and standing from a squat position. The proposed algorithm uses mean absolute value (MAV) and root mean square (RMS) for feature extraction and a multi-layer back propagation neural network (BPN) for predicting the knee angle. The algorithm and the experimental results are both presented. The predicted knee angle can be used to control a lower limb exoskeleton.