Measurement and Analysis of Surface Electromyogram and Handgrip Force

To investigate the relationship of surface electromyogram (sEMG) and handgrip force, a measurement system was developed. Ten healthy subjects were required to perform a series of static contraction trials by maintaining the force level with maximal voluntary contraction (MVC), 75%MVC, 50%MVC and 25%MVC respectively. Then they sustained MVC as long as possible until fatigue. The handgrip force and sEMG on the forearm muscles were recorded. Root mean square (RMS), mean power frequency (MPF) and median frequency (MF) of the sEMG were calculated with LabVIEW. The results show that RMS increased with force level during voluntary contraction, while MPF and MF shift to lower frequency during fatigue condition. These findings suggested that the designed system can be used to study forearm function.

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The Measurement of Sensitivity of Numerical Parameters in Quantification of Local Muscle Fatigue

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193129804201221 ◽

1998 ◽

Vol 42 (12) ◽

pp. 936-939 ◽

Cited By ~ 1

Author(s):

Jung-Yong Kim ◽

Myung-Chul Jung

Keyword(s):

Muscle Fatigue ◽

Voluntary Contraction ◽

Erector Spinae ◽

Superior Performance ◽

Median Frequency ◽

Mean Power ◽

Mean Power Frequency ◽

Zero Crossing ◽

Power Frequency ◽

Local Muscle Fatigue

In order to find the most sensitive Electromyographic (EMG) parameter in quantification of local muscle fatigue (LMF), the first coefficient of Autoregression Model (ARC), Zero Crossing Rate (ZCR), Mean Power Frequency (MPF), Median Frequency (MF) have been analyzed and compared with each other in this study. Ten healthy male subjects participated in the experiment, and EMG signals were collected from the erector spinae muscle continuously for twenty seconds while subjects were isometrically extending their trunk. Various exertion levels such as 15%, 30%, 45%, 60%, and 75% of Maximal Voluntary Contraction (MVC) were also applied to the subjects. As results, ARC was found to be the most sensitive parameter at the level of 15% to 60% of MVC in terms of both slope and R2 value of regression model. On the other hand, MPF and ARC showed the highest R2 value at 60% and 75% level of MVC although MPF scored the lowest slope value at those levels. Moreover, MPF showed a superior performance to MF at 30% to 75% level of MVC.

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Intramuscular and surface electromyogram changes during muscle fatigue

Journal of Applied Physiology ◽

10.1152/jappl.1986.60.4.1179 ◽

1986 ◽

Vol 60 (4) ◽

pp. 1179-1185 ◽

Cited By ~ 258

Author(s):

T. Moritani ◽

M. Muro ◽

A. Nagata

Keyword(s):

Muscle Fatigue ◽

Biceps Brachii ◽

Surface Emg ◽

Firing Frequency ◽

Voluntary Contraction ◽

Mean Power ◽

Mean Power Frequency ◽

Surface Electromyogram ◽

Spike Amplitude ◽

Frequency Power

Twelve male subjects were tested to determine the effects of motor unit (MU) recruitment and firing frequency on the surface electromyogram (EMG) frequency power spectra during sustained maximal voluntary contraction (MVC) and 50% MVC of the biceps brachii muscle. Both the intramuscular MU spikes and surface EMG were recorded simultaneously and analyzed by means of a computer-aided intramuscular spike amplitude-frequency histogram and frequency power spectral analysis, respectively. Results indicated that both mean power frequency (MPF) and amplitude (rmsEMG) of the surface EMG fell significantly (P less than 0.001) together with a progressive reduction in MU spike amplitude and firing frequency during sustained MVC. During 50% MVC there was a significant decline in MPF (P less than 0.001), but this decline was accompanied by a significant increase in rmsEMG (P less than 0.001) and a progressive MU recruitment as evidenced by an increased number of MUs with relatively large spike amplitude. Our data suggest that the surface EMG amplitude could better represent the underlying MU activity during muscle fatigue and the frequency powers spectral shift may or may not reflect changes in MU recruitment and rate-coding patterns.

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The relationship between mean power frequency of the EMG spectrum and muscle fibre conduction velocity

Electroencephalography and Clinical Neurophysiology ◽

10.1016/0013-4694(85)90019-7 ◽

1985 ◽

Vol 60 (2) ◽

pp. 130-134 ◽

Cited By ~ 164

Author(s):

L Arendt-Nielsen ◽

K.R Mills

Keyword(s):

Muscle Fibre ◽

Conduction Velocity ◽

Mean Power ◽

Mean Power Frequency ◽

Muscle Fibre Conduction Velocity ◽

Power Frequency ◽

Emg Spectrum ◽

The Relationship

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Interpretation of EMG spectral alterations and alteration indexes at sustained contraction

Journal of Applied Physiology ◽

10.1152/jappl.1992.73.4.1211 ◽

1992 ◽

Vol 73 (4) ◽

pp. 1211-1217 ◽

Cited By ~ 179

Author(s):

G. M. Hagg

Keyword(s):

Power Spectrum ◽

Action Potential ◽

Median Frequency ◽

Mean Power ◽

Mean Power Frequency ◽

Zero Crossing ◽

Factors Affecting ◽

Fatigue Development ◽

Substantial Progress ◽

Power Frequency

Alterations of the electromyographic power spectrum have been studied extensively to assess fatigue development in the neuromuscular system. Usually, a data reduction has been applied to create an index based on the mean power frequency or the median frequency. The physiological origin of the spectrum alterations has been (and to some extent still is) incompletely known. However, during the 1980s, substantial progress has been made in this field. The factors affecting the electromyographic power spectrum discussed in this review are action potential velocity decrease, firing statistics alterations, action potential modification, muscle temperature, additional recruitment at fatigue, and force level. Their impact on three commonly used fatigue indexes, mean power frequency, median frequency, and zero crossing rate, is also reviewed.

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Features for muscle fatigue computed from electromyogram and mechanomyogram: A new one

Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine ◽

10.1177/0954411916675640 ◽

2016 ◽

Vol 230 (12) ◽

pp. 1096-1105 ◽

Cited By ~ 5

Author(s):

Şükrü Okkesim ◽

Kezban Coşkun

Keyword(s):

Muscle Fatigue ◽

Frequency Ratio ◽

Biceps Brachii ◽

Median Frequency ◽

Mean Power ◽

Mean Power Frequency ◽

Power Frequency ◽

Ratio Change ◽

The Mean ◽

New Feature

Muscle fatigue produces negative effects in the performance and it may lead to a muscle failure. This problem makes the quantitative grading of muscle fatigue a necessity in ergonomic and physiological research. Moreover, the quantitative grading of muscle fatigue is needed to increase work and sport productivity and prevent several accidents that result from muscle fatigue. Even though there are many studies for this aim, there is no quantitative criterion for the evaluation of muscle fatigue. The main reason is that muscle fatigue is a complex physiological situation that is dependent on several parameters. Our aim in this study is to present a new feature to evaluate muscle fatigue and prove the reliability of the new feature by making correlation analyses between this with other features. For this aim, electromyography and mechanomyography signals were simultaneously recorded from the biceps brachii and triceps brachii muscles during the isometric and isotonic contractions of 60 healthy volunteers (30 females, 30 males). The mean power frequency and median frequency, which are used in the literature, were compared to the frequency ratio change, the new measure; correlations between the frequency ratio change and the mean power frequency and median frequency were analysed. There was a high correlation between the features, and frequency ratio change can be used to quantitatively evaluate muscle fatigue.

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Comparison of Endurance Time Prediction of Biceps Brachii Using Logarithmic Parameters of a Surface Electromyogram during Low-Moderate Level Isotonic Contractions

Applied Sciences ◽

10.3390/app11062861 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2861

Author(s):

Chang-ok Cho ◽

Jin-Hyoung Jeong ◽

Yun-jeong Kim ◽

Jee Hun Jang ◽

Sang-Sik Lee ◽

...

Keyword(s):

Biceps Brachii ◽

Maximum Voluntary Contraction ◽

Voluntary Contraction ◽

Endurance Capacity ◽

Mean Power ◽

Mean Power Frequency ◽

Spectral Parameters ◽

Endurance Time ◽

Surface Electromyogram ◽

Isotonic Contractions

At relatively low effort level tasks, surface electromyogram (sEMG) spectral parameters have demonstrated an inconsistent ability to monitor localized muscle fatigue and predict endurance capacity. The main purpose of this study was to assess the potential of the endurance time (Tend) prediction using logarithmic parameters compared to raw data. Ten healthy subjects performed five sets of voluntary isotonic contractions until their exhaustion at 20% of their maximum voluntary contraction (MVC) level. We extracted five sEMG spectral parameters namely the power in the low frequency band (LFB), the mean power frequency (MPF), the high-to-low ratio between two frequency bands (H/L-FB), the Dimitrov spectral index (DSI), and the high-to-low ratio between two spectral moments (H/L-SM), and then converted them to logarithms. Changes in these ten parameters were monitored using area ratio and linear regressive slope as statistical predictors and estimating from onset at every 10% of Tend. Significant correlations (r > 0.5) were found between log(Tend) and the linear regressive slopes in the logarithmic H/L-SM at every 10% of Tend. In conclusion, logarithmic parameters can be used to describe changes in the fatigue content of sEMG and can be employed as a better predictor of Tend in comparison to the raw parameters.

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Muscular endurance and surface electromyogram in isometric and dynamic exercise

Journal of Applied Physiology ◽

10.1152/jappl.1981.51.1.1 ◽

1981 ◽

Vol 51 (1) ◽

pp. 1-7 ◽

Cited By ~ 175

Author(s):

M. Hagberg

Keyword(s):

Maximum Voluntary Contraction ◽

Isometric Exercise ◽

Voluntary Contraction ◽

Dynamic Exercise ◽

Mean Power ◽

Mean Power Frequency ◽

Endurance Time ◽

Significant Difference ◽

Power Frequency ◽

The Mean

In nine male volunteers, the endurance time for sustained isometric exercise (right-angle elbow flexion) and dynamic exercise (continuous concentric and eccentric elbow flexions) was measured at different contraction levels. Intermittent isometric exercises were also performed by four of the subjects in whom surface electromyographic elbow flexor recordings were obtained during the three types of exercise. A rapid decrease of the endurance time was seen at contraction levels above 15–20% of the maximum voluntary contraction for both the sustained isometric and dynamic exercise. There were no significant difference between the regression of the endurance time vs. the contraction level for the sustained isometric exercise and that of the dynamic exercise. However, the endurance time was enhanced in the intermittent isometric exercise compared with the sustained isometric exercise. The development of muscle fatigue was well correlated to change of the myoelectric rootmean-square amplitude and the mean power frequency. Differences in exercise did not significantly affect the relation between the time constant of the mean power frequency decrease and the endurance time.

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Computation and Evaluation of Features of Surface Electromyogram to Identify the Force of Muscle Contraction and Muscle Fatigue

BioMed Research International ◽

10.1155/2014/197960 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 20

Author(s):

Sridhar P. Arjunan ◽

Dinesh K. Kumar ◽

Ganesh Naik

Keyword(s):

Muscle Contraction ◽

Muscle Fatigue ◽

Root Mean Square ◽

Linear Regression Analysis ◽

Maximum Voluntary Contraction ◽

Voluntary Contraction ◽

Median Frequency ◽

Mean Square ◽

Surface Electromyogram ◽

The Relationship

The relationship between force of muscle contraction and muscle fatigue with six different features of surface electromyogram (sEMG) was determined by conducting experiments on thirty-five volunteers. The participants performed isometric contractions at 50%, 75%, and 100% of their maximum voluntary contraction (MVC). Six features were considered in this study:normalised spectral index (NSM5), median frequency, root mean square, waveform length, normalised root mean square (NRMS), and increase in synchronization (IIS) index. Analysis of variance (ANOVA) and linear regression analysis were performed to determine the significance of the feature with respect to the three factors: muscle force, muscle fatigue, and subject. The results show that IIS index of sEMG had the highest correlation with muscle fatigue and the relationship was statistically significant (P<0.01), while NSM5 associated best with level of muscle contraction (%MVC) (P<0.01). Both of these features were not affected by the intersubject variations (P>0.05).

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