A NOVEL ALGORITHM TO PREDICT KNEE ANGLE FROM EMG SIGNALS FOR CONTROLLING A 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 by 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) as features and a multi-layer Back Propagation Neural Network (BPN) for predicting the knee angle. Proposed algorithm along with the experimental results are presented. The predicted knee angle can be used to control a lower limb exoskeleton.