Modeling of Surface EMG Signals using System Identification Techniques

Abstract

Electromyogram (EMG) signal generated by the skeletal muscles during contraction or relaxation plays a significant role in many clinical and biomedical applications. The analysis of EMG signals helps to detect the human intention for movement. One of the major applications of EMG signal is in the control of prosthetic devices and exoskeletons. The aim of this study is to develop a mathematical model with surface EMG (sEMG) signals acquired from the biceps and triceps muscle as input and corresponding angular velocity of motion of forearm as output. The problem that arises while modeling is that the system model is "black-box" model. For solving this problem system identification techniques are used. A linear parametric model called ARX model and a nonlinear model called Hammerstein model are used for system identification and the performances of these system identification models are compared. The platform used for the development and comparison of models is LabVIEW.