Open AccessVARIATION OF INSTANTANEOUS SPECTRAL CENTROID ACROSS BANDS OF SURFACE ELECTROMYOGRAPHIC SIGNALS
Author(s) -
Divya Bharathi Krishnamani,
P. A. Karthick,
Ramakrishnan Swaminathan
Publication year - 2021
Publication title -
biomedical sciences instrumentation
Language(s) - English
Resource type - Journals
ISSN - 1938-1158
DOI - 10.34107/yhpn9422.04356
Subject(s) - electromyography , muscle fatigue , centroid , frequency band , feature (linguistics) , radio spectrum , biceps , acoustics , instantaneous phase , low frequency , pattern recognition (psychology) , mathematics , computer science , artificial intelligence , physics , computer vision , anatomy , medicine , bandwidth (computing) , telecommunications , physical medicine and rehabilitation , linguistics , philosophy , filter (signal processing)
Surface electromyography (sEMG) is a technique which noninvasively acquires the electrical activity of muscles and is widely used for muscle fatigue assessment. This study attempts to characterize the dynamic muscle fatiguing contractions with frequency bands of sEMG signals and a geometric feature namely the instantaneous spectral centroid (ISC). The sEMG signals are acquired from biceps brachii muscle of fifty-eight healthy volunteers. The frequency components of the signals are divided into low frequency band (10-45Hz), medium frequency band (55-95Hz) and high frequency band (95-400Hz). The signals associated with these bands are subjected to a Hilbert transform and analytical shape representation is obtained in the complex plane. The ISC feature is extracted from the resultant shape of the three frequency bands. The results show that this feature can differentiate the muscle nonfatigue and fatigue conditions (p<0.05). It is found the values of ISC is lower in fatigue conditions irrespective of frequency bands. It is also observed that the coefficient of variation of ISC in the low frequency band is less and it demonstrates the ability of handling inter-subject variations. Therefore, the proposed geometric feature from the low frequency band of sEMG signals could be considered for detecting muscle fatigue in various neuromuscular conditions.