TY - GEN
T1 - A new approach for muscle fatigue analysis in young adults at different MVC levels
AU - Al Zaman, Abdullah
AU - Ahad, Mohammad A.
AU - Ferdjallah, Mohammed
AU - Wertsch, Jacqueline J.
PY - 2005
Y1 - 2005
N2 - Spectral parameters such as the mean and the median frequencies have been documented to be reliable outcome variables for the assessment of muscle fatigue. However when recorded for long intervals, electromyography signals become non-stationary. Short-time Fourier transform (STFT) has been extensively used for computing the electromyography time-varying spectrum, but the joint time-frequency resolutions of the STFT is inherently limited. A new time varying autoregressive (TVAR) model is proposed to analyze EMG signals which does not have joint time frequency resolution limitations. The objective of this paper is to examine surface EMG signals of healthy young subjects at different levels of maximum voluntary contractions (MVC) and to analyze spectral shifts in mean frequencies (MNF) using STFT and TV AR models. Our results show, TVAR has a better accuracy in signal representation and high frequency resolution. Further, spectral estimation can be obtained even for shorter data sequence. In this study, continuous stream of EMG data sets from lower extremity muscles are used to characterize muscular fatigue at different MVC level. EMG data were recorded from the Rectus Femoris muscles during isometric contractions.
AB - Spectral parameters such as the mean and the median frequencies have been documented to be reliable outcome variables for the assessment of muscle fatigue. However when recorded for long intervals, electromyography signals become non-stationary. Short-time Fourier transform (STFT) has been extensively used for computing the electromyography time-varying spectrum, but the joint time-frequency resolutions of the STFT is inherently limited. A new time varying autoregressive (TVAR) model is proposed to analyze EMG signals which does not have joint time frequency resolution limitations. The objective of this paper is to examine surface EMG signals of healthy young subjects at different levels of maximum voluntary contractions (MVC) and to analyze spectral shifts in mean frequencies (MNF) using STFT and TV AR models. Our results show, TVAR has a better accuracy in signal representation and high frequency resolution. Further, spectral estimation can be obtained even for shorter data sequence. In this study, continuous stream of EMG data sets from lower extremity muscles are used to characterize muscular fatigue at different MVC level. EMG data were recorded from the Rectus Femoris muscles during isometric contractions.
UR - http://www.scopus.com/inward/record.url?scp=33847138229&partnerID=8YFLogxK
U2 - 10.1109/MWSCAS.2005.1594147
DO - 10.1109/MWSCAS.2005.1594147
M3 - Conference article
AN - SCOPUS:33847138229
SN - 0780391977
SN - 9780780391970
T3 - Midwest Symposium on Circuits and Systems
SP - 499
EP - 502
BT - 2005 IEEE International 48th Midwest Symposium on Circuits and Systems, MWSCAS 2005
T2 - 2005 IEEE International 48th Midwest Symposium on Circuits and Systems, MWSCAS 2005
Y2 - 7 August 2005 through 10 August 2005
ER -