Fatigue Estimation With a Multivariable Myoelectric Mapping Function

MacIsaac, Dawn; Parker, P.A.; Englehart, Kevin; Rogers, D.R.

doi:10.1109/tbme.2006.870220

Cited by 46 publications

(48 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An index of regression signal noise ration (RSNR), also termed SNR by the authors who first used it in muscle fatigue assessment [17], was applied to assess the variability of the complexity statistics in real EMG signal analysis. RSNR has been shown to be effective in evaluation of a muscle fatigue index and is defined as [17]:…”

Section: Real Emg Signalsmentioning

confidence: 99%

Complexity analysis of the biomedical signal using fuzzy entropy measurement

Xie

Chen

et al. 2011

Applied Soft Computing

View full text Add to dashboard Cite

Section: Real Emg Signalsmentioning

confidence: 99%

Complexity analysis of the biomedical signal using fuzzy entropy measurement

Xie

Chen

et al. 2011

Applied Soft Computing

View full text Add to dashboard Cite

“…For each epoch, the EMG signal was normalized and a value for the fApEn and ApEn was calculated according to (16) and (8) Mean frequency so far has been hailed as the gold standard for muscle fatigue assessment by using EMG under 'static' conditions. 26 The slope and intercept of the linear regression for the time course of MNF has served as important quantitative fatigue indices. 12,44 In order to further examine the effectiveness of the fApEn statistic for muscle fatigue assessment, the MNF analysis was also applied to the EMG signals for comparison.…”

Section: Performance On Emg Signalmentioning

confidence: 99%

Fuzzy Approximate Entropy Analysis of Chaotic and Natural Complex Systems: Detecting Muscle Fatigue Using Electromyography Signals

2010

View full text Add to dashboard Cite

Abstract:In the present contribution, a complexity measure is proposed to assess surface 1 electromyography (EMG) in the study of muscle fatigue during sustained, isometric muscle 2 contractions. Approximate entropy (ApEn) is believed to provide quantitative information about the 3 complexity of experimental data that is often corrupted with noise, short data-length, and in many cases, 4 has inherent dynamics that exhibit both deterministic and stochastic behaviors. We developed an 5 improved ApEn measure, i.e., fuzziness approximate entropy (fApEn), which utilizes the fuzzy 6 membership function to define the vectors' similarity. Tests were conducted on independent, identically 7 distributed (i.i.d.) Gaussian and uniform noises, a chirp signal, MIX processes, and Rossler, and Henon 8 maps. Compared with the standard ApEn, the fApEn showed better monotonicity, relative consistency, 9 and more robustness to noise when characterizing signals with different complexities. Performance 10 analysis on experimental EMG signals demonstrated that the fApEn significantly decreased during the 11 development of muscle fatigue, which is a similar trend to that of the mean frequency (MNF) of the 12 EMG signal, while the standard ApEn failed to detect this change. Moreover, the fApEn is more 13 sensitive to muscle fatigue than MNF with a larger linear regression slope (significant value p=0.0213). 14 The results suggest that the fApEn of an EMG signal may potentially become a new reliable method for 15 muscle fatigue assessment and be applicable to other short noisy physiological signal analysis. 16 17

show abstract

“…During sustained contractions, when muscle length and tension are held constant, muscle conduction velocity decreases with fatigue, and this phenomenon is reflected in EMG signals by a decrease of its mean frequency (MNF) [5,19]. MNF so far has been hailed as the gold standard for muscle fatigue assessment by using EMG under 'static' conditions [18]. The slope of the linear regression for the time course of MNF has also served as an important quantitative fatigue index [6,7].…”

Section: Applicationmentioning

confidence: 99%