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.

FATIGUE CONTRACTION ANALYSIS USING EMPIRICAL MODE DECOMPOSITION AND WAVELET TRANSFORM

2015 ◽  
Vol 77 (6) ◽  
Author(s):  
Rubana Haque Chowdhury ◽  
Mamun Bin Ibne Reaz
Keyword(s):  
Wavelet Transform ◽  
Muscle Fatigue ◽  
Empirical Mode Decomposition ◽  
Surface Emg ◽  
Semg Signal ◽  
Emg Signal ◽  
Mode Decomposition ◽  
Spectral Variable ◽  
Walking Exercise ◽  
The Difference

Muscle fatigue is a long lasting reduction of the ability to contract and it is the condition when produced force is reduced. Walking fast can cause muscle fatigue, which is unhealthy and it is incurable when the level of fatigue is high. Muscle fatigue during walk can be determined using several spectral variables. The amplitude and frequency of the surface EMG signal provide a more accurate reflection of motor unit pattern among these spectral variables. This research reports on the effectiveness of Empirical mode decomposition (EMD) and wavelet transform based filtering method applied to the surface EMG (sEMG) signal as a means of achieving reliable discrimination of the muscle fatigue during human walking exercise. In this research, IAV, RMS and AIF values were used as spectral variable. These spectral variables extensively identifies the difference between fatigue and normal muscle when using EMD method compared with other different wavelet functions (WFs). The result shows that the sEMG amplitude and frequency momentously changes from rest position to maximum contraction position.

Age-related differences in calf muscle recruitment strategies in the time-frequency domain during walking as a function of task demand

2021 ◽  
Author(s):  
Hoon Kim ◽  
Jason R. Franz
Keyword(s):  
Older Adults ◽  
Frequency Domain ◽  
Walking Speed ◽  
Principal Component ◽  
Signal Amplitude ◽  
Frequency Content ◽  
Plantar Flexors ◽  
Time Frequency ◽  
Emg Signal ◽  
Age Related

Activation of the plantar flexors is critical in governing ankle push-off power during walking, which decreases due to age. However, electromyographic (EMG) signal amplitude alone are unable to fully characterize motor unit recruitment during functional activity. Although not yet studied in walking, EMG frequency content may also vary due to age-related differences in muscle morphology and neural signaling. Our purpose was to quantify plantar flexor activation differences in the time-frequency domain between young and older adults during walking across a range of speeds and with and without horizontal aiding and impeding forces. Ten healthy young (24.0±3.4 years) and older adults (73.7±3.9 years) walked at three speeds and walked with horizontal aiding and impeding force while muscle activations of soleus (SOL) and gastrocnemius (GAS) were recorded. The EMG signals were decomposed in the time-frequency domain with wavelet transformation. Principal component analyses extracted principal components (PC) and PC scores. Compared to young adults, we observed that GAS activation in older adults: 1) was lower across all frequency ranges during midstance and in slow to middle frequency ranges during push-off, independent of walking speed, and 2) shifted to slower frequencies with earlier timing as walking speed increased. Our results implicate GAS time-frequency content, and its morphological and neural origins, as a potential determinant of hallmark ankle push-off deficits due to aging, particularly at faster walking speeds. Rehabilitation specialists may attempt to restore GAS intensity across all frequency ranges during mid to late stance while avoiding disproportionate increases in slower frequencies during early stance.

scholarly journals The Analysis of Surface EMG Signals with the Wavelet-Based Correlation Dimension Method

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Gang Wang ◽  
Yanyan Zhang ◽  
Jue Wang
Keyword(s):  
Frequency Domain ◽  
Classification Accuracy ◽  
Correlation Dimension ◽  
Surface Emg ◽  
Nonlinear Characteristics ◽  
Time Frequency ◽  
Frequency Domain Methods ◽  
Resolution Level ◽  
Correlation Dimension Method ◽  
Semg Signals

Many attempts have been made to effectively improve a prosthetic system controlled by the classification of surface electromyographic (SEMG) signals. Recently, the development of methodologies to extract the effective features still remains a primary challenge. Previous studies have demonstrated that the SEMG signals have nonlinear characteristics. In this study, by combining the nonlinear time series analysis and the time-frequency domain methods, we proposed the wavelet-based correlation dimension method to extract the effective features of SEMG signals. The SEMG signals were firstly analyzed by the wavelet transform and the correlation dimension was calculated to obtain the features of the SEMG signals. Then, these features were used as the input vectors of a Gustafson-Kessel clustering classifier to discriminate four types of forearm movements. Our results showed that there are four separate clusters corresponding to different forearm movements at the third resolution level and the resulting classification accuracy was 100%, when two channels of SEMG signals were used. This indicates that the proposed approach can provide important insight into the nonlinear characteristics and the time-frequency domain features of SEMG signals and is suitable for classifying different types of forearm movements. By comparing with other existing methods, the proposed method exhibited more robustness and higher classification accuracy.

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