Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
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Muscle Fatigue Assessment using Hilbert-Huang Transform and an Autoregressive Model during Repetitive Maximum Isokinetic Knee Extensions

슬관절의 등속성 최대 반복 신전시 Hilbert-Huang 변환과 AR 모델을 이용한 근피로 평가

  • Kim, H.S. (KSAM, Dept. of Bio-Mechatronic Engineering, Sungkyunkwan University) ;
  • Choi, S.W. (KSAM, Dept. of Bio-Mechatronic Engineering, Sungkyunkwan University) ;
  • Yun, A.R. (Physical Education, Sungshin Women's University) ;
  • Lee, S.E. (Dept. of Bio-Mechatronic Engineering, Sungkyunkwan University) ;
  • Shin, K.Y. (Physical Education, Sungshin Women's University) ;
  • Choi, J.I. (Dept. of Sport and Leisure Studies, Semyung University) ;
  • Mun, J.H. (KSAM, Dept. of Bio-Mechatronic Engineering, Sungkyunkwan University)
  • Published : 2009.04.25
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Abstract

In the working population, muscle fatigue and musculoskeletal discomfort are common, which, in the case of insufficient recovery may lead to musculoskeletal pain. Workers suffering from musculoskeletal pains need to be rehabilitated for recovery. Isokinetic testing has been used in physical strengthening, rehabilitation and post-operative orthopedic surgery. Frequency analysis of electromyography (EMG) signals using the mean frequency (MNF) has been widely used to characterize muscle fatigue. During isokinetic contractions, EMG signals present strong nonstationarities. Hilbert-Haung transform (HHT) and autoregressive (AR) model have been known more suitable than Fourier or wavelet transform for nonstationary signals. Moreover, several analyses have been performed within each active phase during isokinetic contractions. Thus, the aims of this study were i) to determine which one was better suitable for the analysis of MNF between HHT and AR model during repetitive maximum isokinetic extensions and ii) to investigate whether the analysis could be repeated for sequential fixed epoch lengths. Seven healthy volunteers (five males and two females) performed isokinetic knee extensions at $60^{\circ}/s$ and $240^{\circ}/s$ until 50% of the maximum peak torque was reached. Surface EMG signals were recorded from the rectus femoris of the right thigh. An algorithm detecting the onset and offset of EMG signals was applied to extract each active phase of the muscle. Following the results, slopes from the least-square error linear regression of MNF values showed that muscle fatigue of all subjects occurred. The AR model is better suited than HHT for estimating MNF from nonstationary EMG signals during isokinetic knee extensions. Moreover, the linear regression can be extracted from MNF values calculated by sequential fixed epoch lengths (p> 0.0I).

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