In order to detect and assess the muscle fatigue state with the surface electromyography(sEMG) characteristic parameters,this paper carried out a series of isometric contraction experiments to induce the fatigue on th...In order to detect and assess the muscle fatigue state with the surface electromyography(sEMG) characteristic parameters,this paper carried out a series of isometric contraction experiments to induce the fatigue on the forearm muscles from four subjects,and recorded the sEMG signals of the flexor carpi ulnaris.sEMG's median frequency(MDF) and mean frequency(MF) were extracted by short term Fourier transform(STFT),and the root mean square(RMS) of wavelet coefficients in the frequency band of 5—45 Hz was obtained by continuous wavelet transform(CWT).The results demonstrate that both MDF and MF show downward trends within 1 min; however,RMS shows an upward trend within the same time.The three parameters are closely correlated with absolute values of mean correlation coefficients greater than 0.8.It is suggested that the three parameters above can be used as reliable indicators to evaluate the level of muscle fatigue during isometric contractions.展开更多
Surface EMG (electromyography) signal is a complex nonlinear signal with low signal to noise ratio (SNR). This paper is aimed at identifying different patterns of surface EMG signals according to fractal dimension. Tw...Surface EMG (electromyography) signal is a complex nonlinear signal with low signal to noise ratio (SNR). This paper is aimed at identifying different patterns of surface EMG signals according to fractal dimension. Two patterns of surface EMG signals are respectively acquired from the right forearm flexor of 30 healthy volunteers during right forearm supination (FS) or forearm pronation (FP). After the high frequency noise is filtered from surface EMG signal by a low-pass filter, fractal di-mension is calculated from the filtered surface EMG signal. The results showed that the fractal dimensions of filtered FS surface EMG signals and those of filtered FP surface EMG signals distribute in two different regions, so the fractal dimensions can rep-resent different patterns of surface EMG signals.展开更多
The purpose of this study is to investigate different factors of the artifact in surface electromyography(EMG) signal caused by functional electrical stimulation(FES). The factors investigated include the size of stim...The purpose of this study is to investigate different factors of the artifact in surface electromyography(EMG) signal caused by functional electrical stimulation(FES). The factors investigated include the size of stimulation electrode pads, the amplitude, frequency, and pulse width of the stimulation waveform and the detecting electrode points. We calculate the root mean square(RMS) of EMG signal to analyze the effect of these factors on the M-wave properties. The results indicate that the M-wave mainly depends on the stimulation amplitude and the distribution of detecting electrodes,but not on the other factors. This study can assist the reduction of artifact and the selection of detecting electrode points.展开更多
基金Supported by the National Natural Science Foundation of China(No.81222021 and No.31011130042)the National Key Technology R&D Program of the Ministry of Science and Technology of China(No.2012BAI34B02)
文摘In order to detect and assess the muscle fatigue state with the surface electromyography(sEMG) characteristic parameters,this paper carried out a series of isometric contraction experiments to induce the fatigue on the forearm muscles from four subjects,and recorded the sEMG signals of the flexor carpi ulnaris.sEMG's median frequency(MDF) and mean frequency(MF) were extracted by short term Fourier transform(STFT),and the root mean square(RMS) of wavelet coefficients in the frequency band of 5—45 Hz was obtained by continuous wavelet transform(CWT).The results demonstrate that both MDF and MF show downward trends within 1 min; however,RMS shows an upward trend within the same time.The three parameters are closely correlated with absolute values of mean correlation coefficients greater than 0.8.It is suggested that the three parameters above can be used as reliable indicators to evaluate the level of muscle fatigue during isometric contractions.
基金Project supported by the National Natural Science Foundation of China (No. 60171006)the National Basic Research Program (973) of China (No. 2005CB724303)
文摘Surface EMG (electromyography) signal is a complex nonlinear signal with low signal to noise ratio (SNR). This paper is aimed at identifying different patterns of surface EMG signals according to fractal dimension. Two patterns of surface EMG signals are respectively acquired from the right forearm flexor of 30 healthy volunteers during right forearm supination (FS) or forearm pronation (FP). After the high frequency noise is filtered from surface EMG signal by a low-pass filter, fractal di-mension is calculated from the filtered surface EMG signal. The results showed that the fractal dimensions of filtered FS surface EMG signals and those of filtered FP surface EMG signals distribute in two different regions, so the fractal dimensions can rep-resent different patterns of surface EMG signals.
基金State Key Laboratory of Robotics and Systemgrant number:SKLRS-2012-ZD-04+1 种基金Natural Science Foundation of Shanghaigrant number:14ZR1421300
文摘The purpose of this study is to investigate different factors of the artifact in surface electromyography(EMG) signal caused by functional electrical stimulation(FES). The factors investigated include the size of stimulation electrode pads, the amplitude, frequency, and pulse width of the stimulation waveform and the detecting electrode points. We calculate the root mean square(RMS) of EMG signal to analyze the effect of these factors on the M-wave properties. The results indicate that the M-wave mainly depends on the stimulation amplitude and the distribution of detecting electrodes,but not on the other factors. This study can assist the reduction of artifact and the selection of detecting electrode points.
