An EMG fractal indicator having different sensitivities to changes in force and muscle fatigue during voluntary static muscle contractions

Ravier, Philippe; Buttelli, Olivier; Jennane, Rachid; Couratier, Pierre

doi:10.1016/j.jelekin.2004.08.008

Cited by 54 publications

(46 citation statements)

References 27 publications

(35 reference statements)

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“…Other research found that maximal elbow angles would result in high RMS values in isometric contractions [10]. In addition, changes in the elbow angle (for isometric contractions) gives a shift in the median frequencies of the EMG signal at fatigue occurrence [5]. The findings in these previous studies, as well as the performance of the different feature extraction methods in this study, showed that these parameters are useful in finding the optimal angle.…”

Section: Discussionsupporting

confidence: 66%

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Optimal Elbow Angle for Extracting sEMG Signals During Fatiguing Dynamic Contraction

2015

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Surface electromyographic (sEMG) activity of the biceps muscle was recorded from 13 subjects. Data was recorded while subjects performed dynamic contraction until fatigue and the signals were segmented into two parts (Non-Fatigue and Fatigue). An evolutionary algorithm was used to determine the elbow angles that best separate (using Davies-Bouldin Index, DBI) both Non-Fatigue and Fatigue segments of the sEMG signal. Establishing the optimal elbow angle for feature extraction used in the evolutionary process was based on 70% of the conducted sEMG trials. After completing 26 independent evolution runs, the best run containing the optimal elbow angles for separation (Non-Fatigue and Fatigue) was selected and then tested on the remaining 30% of the data to measure the classification performance. Testing the performance of the optimal angle was undertaken on nine features extracted from each of the two classes (Non-Fatigue and Fatigue) to quantify the performance. Results showed that the optimal elbow angles can be used for fatigue classification, showing 87.90% highest correct classification for one of the features and on average of all eight features (including worst performing features) giving 78.45%.

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Section: Discussionsupporting

confidence: 66%

“…• in the biceps brachii to analyse fluctuations, which indicate fatigue in the sEMG signal, based on a shift in the median frequencies [5]. In muscle fatigue research a goniometer is often used together with other signal detection methods, yielding additional useful information about the fatiguing muscle.…”

Section: Introductionmentioning

confidence: 99%

Optimal Elbow Angle for Extracting sEMG Signals During Fatiguing Dynamic Contraction

2015

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“…These signals are recorded and then processed with a power spectrum analysis. The median frequency (MF) is defined as the frequency at which the spectrum is divided into two equal parts [3]. The typical MF range is from 70 to 120 Hz [4], which corresponds to a time scale of 10 to 20 ms.…”

Section: Introductionmentioning

confidence: 99%

“…Another study found that amplitude-related characteristics of motor units can help to avoid misleading interpretations of sEMG changes [11]. Ravier et al pursued a different approach and fitted the sEMG frequency spectrum to P( f ) 1/f α behavior, where P is the power of the signal, f is the frequency, and α > 0 is an exponent [3]. Such behavior was originally derived for "self-organized" systems such as avalanches [12].…”

Section: Introductionmentioning

confidence: 99%

Comparison of spectral and entropic measures for surface electromyography time series: A pilot study

Sung¹,

Zürcher²,

Kaufman³

2007

JRRD

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Abstract-In a previous study, we reported that the mean square displacement calculated from the surface electromyography (sEMG) signal of low back muscles exhibits a plateaulike behavior for intermediate times 20 ms < t < 400 ms. This property indicates the existence of correlations in the signal for times much longer than the inverse of the median frequency (MF), which is calculated from the power spectrum 1/ = 1/(100 Hz) = 10 ms, where is the MF. This result suggests the use of methods from nonlinear analysis to characterize sEMG time series. In this study, we applied these techniques to sEMG signals and calculated the time-dependent entropy. The results showed that the entropy of physiological time series from nondisabled control subjects is higher than the entropy from subjects with low back pain (LBP). The entropy reveals properties of the sEMG signal that are not captured by the power spectrum. In turn, this suggests a possible benefit of entropy as a tool for the clinical assessment of LBP. Because the two groups of subjects were not matched by age, the physiological origin of the observed differences between groups could be attributed to either LBP, age, or both. Additional studies with larger sample sizes and age-matched subjects are needed to investigate the relationship between LBP and entropy.

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“…Some others recent works [1,2,5,8,12] present a fractal structure of the muscle, which consolidate the approach based on the use of non integer (or fractal) model to characterize its dynamic behaviour. A fractional structure model, due to its infinite dimension nature, is particularly adapted to model complex systems with few parameters, and to obtain real time exploitable model: muscle complexity can be described thanks to fractional models, with a model containing a reduced number of parameters.…”

Section: Introductionmentioning

confidence: 98%

Fractional Model of a Gastrocnemius Muscle for Tetanus Pattern

Sommacal

Melchior

Cabelguen

et al. 2005

Volume 6: 5th International Conference on Multibody Systems, Nonlinear Dynamics, and Control, Parts A, B, and C

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This study talks about gastrocnemius muscle identification. During biological activation, every contractile structure is unsynchronized. Likewise, contraction and relaxation phases depend on all contractile elements, the activation type and the state of health. Moreover, gastrocnemius muscle is composed of three fibre types: Fast Fatigable (FF), Fast Resistant (FR) and Slow (S) fibres.Some recent works highlight a fractal structure of the muscle, which consolidate the approach based on the use of a non integer (or fractal) model to characterize its dynamic behavior. A fractional structure model, due to its infinite dimension nature, is particularly adapted to model complex systems with few parameters and to obtain a real time exploitable model. According to its complexity, muscle structure and activation mechanisms, and to these previous considerations, an identification based on fractional model is presented.A model is proposed for the tetanus pattern response in a high tiredness state. It is based on a multi-model structure, which corresponds to the decomposition in contraction and relaxation phases. This multi-model structure is expected to be included subsequently in agonist-antagonist structure.

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An EMG fractal indicator having different sensitivities to changes in force and muscle fatigue during voluntary static muscle contractions

Cited by 54 publications

References 27 publications

Optimal Elbow Angle for Extracting sEMG Signals During Fatiguing Dynamic Contraction

Optimal Elbow Angle for Extracting sEMG Signals During Fatiguing Dynamic Contraction

Comparison of spectral and entropic measures for surface electromyography time series: A pilot study

Fractional Model of a Gastrocnemius Muscle for Tetanus Pattern

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