Muscle fatigue analysis using surface EMG signals and time–frequency based medium‐to‐low band power ratio

2016 ◽  
Vol 52 (3) ◽  
pp. 185-186 ◽  
Author(s):  
P.A. Karthick ◽  
S. Ramakrishnan
Keyword(s):  
Muscle Fatigue ◽  
Surface Emg ◽  
Fatigue Analysis ◽  
Power Ratio ◽  
Time Frequency ◽  
Band Power

Lumbar Muscle Fatigue Analysis Using Sorensen Test with Different Upper Body Offload Conditions

2018 ◽  
Vol 62 (1) ◽  
pp. 879-881
Author(s):  
Rong Huangfu ◽  
Sean Gallagher ◽  
Phillip Whitley ◽  
Richard Sesek ◽  
Mark Schall ◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Surface Emg ◽  
Fatigue Analysis ◽  
Trunk Muscles ◽  
Upper Body ◽  
Median Frequency ◽  
Lumbar Region ◽  
Isometric Endurance ◽  
Lumbar Muscle ◽  
Fatigue Characteristics

The Sorensen test has been widely used in assessing the isometric endurance of trunk muscles. In this study, a modified Sorensen test was performed using four different upper body offload conditions (0%, 25% 50% and 75%) and surface EMG (sEMG) was used to determine fatigue characteristics in lumbar muscles. Results showed that the offloading of the upper body has a statistically significant impact on the slope of the EMG median frequency, which is representative of lumbar muscle fatigue responses. The first 25% offloading significantly decreased the fatigue response in the lumbar region. In addition, the 25% offload condition was significantly different from the 75% offload condition. However, the slopes of 25% and 50% offload conditions are not statistically different from each other and same for 50% and 75% offload conditions.

The Use of Surface Electromyography in Muscle Fatigue Assessments–A Review

2015 ◽  
Vol 74 (6) ◽  
Author(s):  
Nurul Asyikin Kamaruddin ◽  
Puspa Inayat Khalid ◽  
Ahmad Zuri Shaameri
Keyword(s):  
Muscle Fatigue ◽  
Frequency Domain ◽  
Surface Electromyography ◽  
Sports Injuries ◽  
Surface Emg ◽  
Time Frequency ◽  
Emg Signal ◽  
Assessment Techniques ◽  
Fatigue Estimation ◽  
Interaction Surface

The developments in physiological studies have established the importance of muscle fatigue estimation in various aspects including neurophysiological and medical research, rehabilitation, ergonomics, sports injuries and human-computer interaction. Surface electromyography signals are commonly used in muscle fatigue assessment. Techniques of surface EMG signal processing used to quantify muscle fatigue are not only based on time domain and frequency domain, but also on time–frequency domain. The developments of different signal analysis to extract different indices for muscle fatigue assessments are reviewed in this paper. Several indices in time, frequency, and time-frequency representations for muscle fatigue assessments have been identified. However the sensitivity of those indices needs to be investigated. Minimizing this issue becomes the objective of the recent research in muscle fatigue assessments.

scholarly journals Sensor-Level Wavelet Analysis Reveals EEG Biomarkers of Perceptual Decision-Making

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2461
Author(s):  
Alexander Kuc ◽  
Vadim V. Grubov ◽  
Vladimir A. Maksimenko ◽  
Natalia Shusharina ◽  
Alexander N. Pisarchik ◽  
...  
Keyword(s):  
Decision Making ◽  
Sensory Information ◽  
Stimulus Onset ◽  
Perceptual Process ◽  
Perceptual Decision Making ◽  
Beta Band ◽  
Perceptual Decision ◽  
Time Frequency ◽  
Band Power ◽  
Sensory Features

Perceptual decision-making requires transforming sensory information into decisions. An ambiguity of sensory input affects perceptual decisions inducing specific time-frequency patterns on EEG (electroencephalogram) signals. This paper uses a wavelet-based method to analyze how ambiguity affects EEG features during a perceptual decision-making task. We observe that parietal and temporal beta-band wavelet power monotonically increases throughout the perceptual process. Ambiguity induces high frontal beta-band power at 0.3–0.6 s post-stimulus onset. It may reflect the increasing reliance on the top-down mechanisms to facilitate accumulating decision-relevant sensory features. Finally, this study analyzes the perceptual process using mixed within-trial and within-subject design. First, we found significant percept-related changes in each subject and then test their significance at the group level. Thus, observed beta-band biomarkers are pronounced in single EEG trials and may serve as control commands for brain-computer interface (BCI).

Intramuscular and surface electromyogram changes during muscle fatigue

1986 ◽  
Vol 60 (4) ◽  
pp. 1179-1185 ◽  
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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