Trans. Tianjin Univ. 2014, 20: 139-143

DOI 10.1007/s12209-014-2181-2

Accepted date: 2013-07-19. *Supported by the National Natural Science Foundation of China (No. 81222021 and No. 31011130042) and the National Key Technology R&D

Program of the Ministry of Science and Technology of China (No. 2012BAI34B02). Ming Dong, born in 1976, male, Dr, Prof. Correspondence to Ming Dong, E-mail: richardming@tju.edu.cn.

sEMG Feature Analysis on Forearm Muscle Fatigue During Isometric Contractions*

Ming Dong(明 东)，Wang Xin(王 欣)，Xu Rui(徐 瑞)，Qiu Shuang(邱 爽)，

Zhao Xin(赵 欣)，Qi Hongzhi(綦宏志)，Zhou Peng(周 鹏)，

Zhang Lixin(张力新)，Wan Baikun(万柏坤)

(Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, School of Precision Instrument and Opto-

Electronics Engineering, Tianjin University, Tianjin 300072, China)

© Tianjin University and Springer-Verlag Berlin Heidelberg 2014

Abstract：In order to detect and assess the muscle fatigue state with the surface electromyography (sEMG) character-istic parameters, this paper carried out a series of isometric contraction experiments to induce the fatigue on the fore-arm 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 corre-lation 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. Keywords：muscle fatigue; isometric contraction; time-frequency spectrum analysis; median frequency; mean fre-quency; root mean square; correlation

Continuous or overloaded muscle contractions for a

long time may induce muscle fatigue[1,2], in which mus-cles become unable to generate the force required and a series of corresponding physiological characteristics are weakened, such as excitability, conductivity and contrac-tility[3-6]. As isometric contractions are easier to induce muscle fatigue than voluntary contractions or dynamic contractions[7], reliable methods or indexes to estimate the level as well as the procedure of muscle fatigue dur-ing isometric contractions are needed. According to the previous studies, electromyography (EMG) signal ampli-tude increases and spectrum shifts to lower frequency band with the increase of muscle fatigue degree during isometric contractions[8,9]. EMG's median frequency (MDF) and mean frequency (MF) in the frequency spectrum were usually used as indicators of spectrum shifting[10-12] and the root mean square (RMS) of EMG's wavelet coef-ficients was used to determine the amplitude of surface electromyography (sEMG)[13,14]. The above parameters can be used to evaluate the muscle fatigue from multiple

perspectives such as power distribution, frequency distri-bution and amplitude distribution. The purpose of this paper is to investigate the correlationship of MDF, MF and RMS during the muscle fatigue procedure.

A series of isometric contraction experiments to in-duce fatigue on the forearm muscle from four subjects were carried out and sEMG data of the flexor carpi ul-naris were recorded for signal processing. Frequency spectrum analysis and wavelet transform were applied to extract sEMG's characteristics, then their correlations were discussed.

1 Methodology

1.1 Short term Fourier transform Frequency spectrum analysis was applied to extract

sEMG's MDF and MF by short term Fourier transform (STFT)[15]. The STFT of signal x(t) is defined as

j2π( , ) ( ) ( )e dfX t f x t h t

(1)

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where t is time; f is frequency; h(t) is the window func-tion. 2

( , )X t f 2 can be viewed as the energy of x(t) at frequency f and time t. A new variable P(t, f) was intro-duced to indicate the energy at each time and frequency pair, which is known as the spectrogram of x(t), i.e.,

2( , ) | ( , ) |P t f X t f (2)

1.2 MDF and MF The power spectral density (PSD)[15] of the signal is

defined as follows:

j( ) ( )e dftp f r t t

(3)

where r(t) is the autocorrelation function. The MDF is defined by Eq. (4), i.e.,

0MDF

0 MDF

( )d ( )df

p f f p f f (4)

The MF is calculated with Eq. (5), i.e.,

0

0

0

0

( )dMF

( )d

f

f

fp f f

p f f

(5)

where f0 is the upper limit frequency of PSD, i.e., half of the sampling frequency. 1.3 RMS

The RMS of wavelet coefficients in low frequency bands are used as a general indicator to determine the amplitude of the signal[16,17]. Wavelet coefficients Wx(a,τ) are calculated with Eq.(6), i.e.,

1, dx

tW a x t t

aa

- (6)

where a＞0 is the scale parameter; and ψ(t) is the mother wavelet. 1.4 Correlation coefficient For variables X and Y,

1, 1, 2, ,iX X i N (7)

2, 1, 2, ,jY Y j N (8)

The correlation coefficient r ranging from -1 to 1 is de-fined as

1

2 2

1 1

( )( )

( ) ( )

N

i ii

N N

i ii i

X X Y Yr

X X Y Y

(9)

where X and Y are the mean values of iX and iY , re-spectively. Usually, the two variables are considered to have an obvious correlation when r ＞0.8[18].

2 Experimental procedure

2.1 Subjects Four healthy male subjects volunteered for the ex-

periments, with their mean weight, height and age of 66.8±6.2 kg, 172.2±5.2 cm and 23.5±1.0 years respec-tively. All the subjects were right-handed. Before the start, all the volunteers were informed of the experimen-tal requirements, procedures and details to ensure a better involvement. In addition, the volunteers were instructed not to do heavy exercises 24 h prior to the experiment to exclude the influence of residual fatigue. 2.2 Data acquisition

The schematic of the measuring arrangement is shown in Fig. 1(a). Each subject was seated in front of the experimental table, where the grip sensor (BK-

WL120, Beijing Aerospace HENGLI Measurement and Control Technology Development Co., Ltd, China) and the oscilloscope (TSD2012B, Tektronix, USA) were placed. The oscilloscope was used to provide the subject with a direct-vision feedback and guidance. The subject’s trunk kept upright, the right forearm unbent forward and the right elbow straight. The right hands of the subjects clenched the grip sensor, with the palm facing down.

(a) Schematic of measurement

(b) Locations of electrodes

Fig. 1 Schematic of measuring arrangement

Afterwards, the flexor carpi ulnaris of the right fore-arm was exposed. Two sEMG electrodes were positioned

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slightly on the fullest part of the muscle belly. The loca-tions of electrodes are shown in Fig. 1(b). The direction of the line which connected the center of one electrode with the center of the other was in parallel with the direc-tion of the long axis of the muscle fibers. The distance between two centers was 15 mm. One reference electrode was positioned on the pisiform bone, as shown in Fig. 1(b). Before the electrodes were pasted, the skin was sandpapered and cleaned to reduce the impedance be-tween the electrodes and the skin, and to prevent the elec-trodes from moving as much as possible. Then, the elec-trodes were pasted with electrode cream, and the wires were fixed with medical adhesive plaster.

Before the experiments, the maximum voluntary contraction (MVC) torque was measured. The subjects were asked to clench fists with their maximal strength. The level of the force was displayed on the oscilloscope, which was connected to the grip sensor in order to give the subject a feedback. Three trails were made, each lasted 45 s, and a two-min break was taken. The mean of three MVCs was calculated as the reference value.

When the preparation work was finished, the ex-periments began. Each subject clenched grip sensor and kept the level of the power as stable as 60% MVC and as long as possible until he was exhausted. Meantime, sEMG was input into the amplifier (PRE-ISO· EMG S08, Xiangyun Computer Technology Corporation, China), then sampled by an A/D convertor and recorded by the computer. The sampling rate was 1 000 Hz. 2.3 Data analysis

First, STFT was used to do spectral analysis of sEMG. The sEMG signal during the process of clenching a fist was obtained. Note that the time when grip strength rose to 60% MVC for the first time was regarded as the starting point and the time when grip strength began to decline was regarded as the end point. A time window of 1 024 points (1 s) in STFT was taken to do spectrogram of sEMG signal, which displays the energy at each time and frequency pair.

Second, MDF and MF were calculated as two char-acteristic parameters to indicate spectrum shifting.

Third, CWT was made to decompose the signal into different frequency bands with the mother wavelet of six-

order Symlets (Sym6). The wavelet coefficients of 5—45 Hz were used to determine the amplitude of sEMG by calculating the RMS with a time window of 0.5 s. In ad-dition, a 2-order low-pass Butterworth filter (25 Hz) was used to smooth the RMS sequence.

At last, the correlation coefficients between each pair of MDF, MF and RMS were calculated.

3 Results and discussion

3.1 Time-frequency spectrum The subject kept relaxed for almost 15 s, clenching a

fist with the constant force of 60% MVC for 45 s, and relaxed for 10 s again. The typical result of time-

frequency spectrum by STFT is shown in Fig. 2. It can be seen that the main frequency components of sEMG con-centrate in 25—150 Hz. The energy of high frequency components declines slightly; however, the energy of low frequency components (15—50 Hz) grows obviously with the increase of fatigue degree. These results also confirm that the power spectrum of sEMG would shift to the low frequency band with the increase of muscle fa-tigue[10-12].

Fig. 2 Time-frequency spectrum of sEMG during iso-metric contractions

3.2 Changes of MDF and MF The changes of MDF and MF with time are shown

in Fig. 3 and Fig. 4, respectively. In order to better de-scribe the trends, linear regression[18] was adopted. For the independent variable x and dependent variable y, their linear relationship is estimated by Eq. (10), i.e.,

1 0y b x b (10)

where b0 and b1 are parameters to be determined during

Fig. 3 Changes of MDF during isometric contractions

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linear regression. The coefficients b1 of both MDF and MF are negative, which mean downward trends with time, as shown in Fig. 3 and Fig. 4. Compared with MF, the slope for MDF is deeper. In other words, MDF is more sensitive in the evaluation of muscle fatigue during isometric contractions.

Fig. 4 Changes of MF during isometric contractions

3.3 Variation of RMS For wavelet analysis, a 60-s sEMG signal was cho-

sen. Fig. 5(a) shows the RMS of the wavelet coefficients obtained by CWT in the frequency of 5—45 Hz, while Fig. 5(b) is the RMS after low-pass filtering. As shown in Fig. 5, there is an upward trend of RMS with time. The obvious upward trend of the RMS, especially after filter-ing, is in accordance with the results from previous stud-ies[13,14].

(a) Before low-pass filtering

(b) After low-pass filtering

Fig. 5 RMS of wavelet coefficients from low frequency components during isometric contractions

3.4 Correlation coefficients of MDF, MF and RMS

The correlation coefficients between each pair of MDF, MF and RMS for all subjects are listed in Tab. 1. All the absolute values of the three mean correlation co-efficients are larger than 0.8, which means strong correla-tions between the three parameters of the sEMG on fore-arm muscle fatigue during isometric contractions. The absolute values ( r ) of RMS-MDF and RMS-MF are a little smaller than that of MDF-MF, as shown in Tab. 1, which may be due to different physical meanings.

Despite the strong correlation existing among the three parameters, it is difficult to work out a typical index for the fatigue state. This is probably because the muscu-lar endurances and muscular forces differ among sub-jects, and the sensitivities of three characteristics to fa-tigue are different. More subjects are needed to avoid the impact of individual variation.

Tab. 1 Correlation coefficients between each pair of the three parameters for 4 subjects

Subject RMS-MDF RMS-MF MDF-MF

1

2

3

4

-0.834

-0.832

-0.853

-0.746

-0.867

-0.847

-0.859

-0.726

0.912

0.962

0.978

0.945

Mean -0.816±0.047 -0.825±0.066 0.949±0.028

In addition, as muscle activity is generally accom-panied by fatigue with a long time, it is an ideal state to be active without fatigue. When the experiments just started and muscles suffered less fatigue, we observed that the three characteristics were relatively stable and looked like normal sequences. However, based on the above analysis, we cannot conclude that the correlation of the three parameters exists. In the future work, we will conduct experiments to verify the correlation of the three parameters when muscles suffer less fatigue.

4 Conclusions

A series of muscle fatigue experiments of the right forearm under isometric contractions were performed on four subjects, and sEMG signals of the flexor carpi ul-naris were processed by STFT and CWT. sEMG's MDF and MF were extracted by STFT, and sEMG's wavelet coefficients in 5—45 Hz frequency band were obtained by CWT. The sEMG's time-frequency spectrogram shows that the main frequency components of sEMG

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concentrate in 25—150 Hz and the low frequency com-ponents of sEMG's PSD grow obviously with the in-crease of fatigue degree. An interesting result by linear regression is that the changes of MDF and MF both show downward trends, but the RMS shows an upward trend obviously. Moreover, these three characteristic parame-ters have mean correlation coefficients with the absolute values larger than 0.8.

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(Editor: Wu Liyou)