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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?s and 240?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>0.01).

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참고문헌 (16건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 A new look at the statistical model identification 네이버 미소장
2 Quantification of human dynamic muscle fatigue by electromyography and kinematic profiles 네이버 미소장
3 The influence of electrode placement over the innervation zone on electromyographic amplitude and mean power frequency versus isokinetic torque relationships 네이버 미소장
4 Capponi, M., T. D’Alessio and M. Laurenti. 1988. Sequential estimation of power spectra for non stationary myoelectric signals. Electroencephalography & Clinical Neurophysiology 28:427-432. 미소장
5 NEUROMUSCULAR RESPONSES TO THREE DAYS OF VELOCITY-SPECIFIC ISOKINETIC TRAINING 네이버 미소장
6 Criterion validation of surface EMG variables as fatigue indicators using peak torque: a study of repetitive maximum isokinetic knee extensions. 네이버 미소장
7 Musculo-Skeletal Disorders of the Farm Women While Performing the Top Dressing of Fertilizer Activity 네이버 미소장
8 A comparison of computer-based methods for the determination of onset of muscle contraction using electromyography. 네이버 미소장
9 Huang, N. E., Z. Shen, S. R. Long, M. L. C. Wu, H. H. Shih, Q. N. Zheng, N. C. Yen, C. C. Tung and H. H. Liu. 1998. The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis. Proceeding of the Royal Society of London Series A 454:903-995. 미소장
10 Spectrum analysis—A modern perspective 네이버 미소장
11 Knaflitz, M. and P. Bonato. 1999. Time-frequency methods applied to muscle fatigue assessment during dynamic contractions. Journal of Electromyography Kinesiology 9(5):337-350. 미소장
12 MacIsaac, D., P. A. Parker, R. N. Scott. 2001. The short-time Fourier transform and muscle fatigue assessment in dynamic contractions. Journal of Electromyography and Kinesiology 11(6):439-449. 미소장
13 National occupational research agenda (NORA) future directions in occupational musculoskeletal disorder health research. 네이버 미소장
14 Electrical manifestations of muscle fatigue during concentric and eccentric isokinetic knee flexion-extension movements 네이버 미소장
15 Musculoskeletal disorders in farmers and farm workers 네이버 미소장
16 Mean frequency derived via Hilbert-Huang transform with application to fatigue EMG signal analysis 네이버 미소장