TY - GEN
T1 - Spectral analysis of sEMG signals to investigate skeletal muscle fatigue
AU - Kumar, Parmod
AU - Sebastian, Anish
AU - Potluri, Chandrasekhar
AU - Yihun, Yimesker
AU - Anugolu, Madhavi
AU - Creelman, Jim
AU - Urfer, Alex
AU - Naidu, D. Subbaram
AU - Schoen, Marco P.
PY - 2011
Y1 - 2011
N2 - Our recent investigations are focused to develop dynamic models for skeletal muscle force and finger angles for prosthetic hand control using surface electromyographic sEMG as input. Since sEMG is temporal and spatially distributed and is influenced by various factors, muscle fatigue and its related sEMG becomes of importance. This study is an effort to spectrally analyze the sEMG signal during progression of muscle fatigue. The sEMG is captured from the arms of healthy subjects during muscle fatiguing experiments for dynamic and static force levels. Filtered sEMG signal is segmented in five parts with 75% overlap between adjacent segments. The analysis is done using different classical (fast Fourier transform, Welch's averaged modified periodogram), model-based (Yule-Walker, Burg, Covariance and Modified Covariance autoregressive (AR) method), and eigenvector methods (Multiple Signal Classification (MUSIC) and eigenvector spectral estimation method) in frequency domain. Results show that the classical and eigenvector based methods are more sensitive than the model-based methods to fatigue related changes in sEMG signals.
AB - Our recent investigations are focused to develop dynamic models for skeletal muscle force and finger angles for prosthetic hand control using surface electromyographic sEMG as input. Since sEMG is temporal and spatially distributed and is influenced by various factors, muscle fatigue and its related sEMG becomes of importance. This study is an effort to spectrally analyze the sEMG signal during progression of muscle fatigue. The sEMG is captured from the arms of healthy subjects during muscle fatiguing experiments for dynamic and static force levels. Filtered sEMG signal is segmented in five parts with 75% overlap between adjacent segments. The analysis is done using different classical (fast Fourier transform, Welch's averaged modified periodogram), model-based (Yule-Walker, Burg, Covariance and Modified Covariance autoregressive (AR) method), and eigenvector methods (Multiple Signal Classification (MUSIC) and eigenvector spectral estimation method) in frequency domain. Results show that the classical and eigenvector based methods are more sensitive than the model-based methods to fatigue related changes in sEMG signals.
UR - http://www.scopus.com/inward/record.url?scp=84860686563&partnerID=8YFLogxK
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U2 - 10.1109/CDC.2011.6161297
DO - 10.1109/CDC.2011.6161297
M3 - Conference contribution
AN - SCOPUS:84860686563
SN - 9781612848006
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 47
EP - 52
BT - 2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
Y2 - 12 December 2011 through 15 December 2011
ER -