FRACTAL PROPERTIES OF SURFACE ELECTROMYOGRAM FOR CLASSIFICATION OF LOW-LEVEL HAND MOVEMENTS FROM SINGLE-CHANNEL FOREARM MUSCLE ACTIVITY

Abstract

Surface electromyogram (sEMG) has been used in the identification of various hand movements which can lead to a number of rehabilitation, medical, and human computer interface applications. These applications are currently in need of higher accuracy and become challenging because of its unreliability in the classification of sEMG when the level of muscle contraction is low and when there are multiple active muscles. The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when muscles are weakly active such as during sustained wrist and finger flexion. This study reports the use of fractal properties of sEMG to identify the small changes in strength of muscle contraction and the location of the active muscles. It is observed that the fractal dimension (FD) of the signal is related to the complexity of the muscle contraction while maximum fractal length (MFL) is related to the strength of contraction of the associated muscle. The results show that the MFL and FD of a single-channel sEMG from the forearm can be used to accurately identify a set of finger-and-wrist flexion-based actions even when the muscle activity is very weak. It is proposed that such a system could be used to control a prosthetic hand or for a human computer interface.