**2.1. The definition of mean and median frequencies**

MNF is an average frequency which is calculated as the sum of product of the EMG power spectrum and the frequency divided by the total sum of the power spectrum (e.g. Oskoei & Hu, 2008; Phinyomark et al., 2012a). MNF has a similar definition as several features, i.e. the central frequency (*fc*), centroid and the spectral center of gravity, in a number of studies (Du & Vuskovic, 2004; Farina & Merletti, 2000). In addition, MNF is also called as mean power frequency and mean spectral frequency in several works. The definition of MNF is given by

Computational Intelligence in Electromyography Analysis – 196 A Perspective on Current Applications and Future Challenges

chapter.

presented in Section 7.

(joint angle) should have paid more attention (Cechetto et al., 2001; Doheny et al., 2008). In the literature, such effects on MNF and MDF have still been inconclusive (Doheny et al., 2008; Phinyomark et al., 2012c). A summary of the conflicting results mentioned in the literature is also presented. The possible reasons for the conflicting results in both effects are discussed. In addition to the clinical applications, the classification of EMG signals during upper-limb movements for using in the engineering applications (Oskoei & Hu, 2007) is proposed in this

The rest of this chapter is as follows: Section 2 presents the principle and theory of MNF and MDF, and the relations between MNF (and MDF) and other EMG frequency-domain features are also described and discussed. In Section 3, the extensive review and careful survey of the up-to-date experiments for the assessing muscle fatigue using MNF and MDF in numerous applications are summarized, and moreover, the recent trend of MNF and MDF in the assessment of muscle fatigue is discussed in this section. On the other hand, the effects of muscle force and muscle geometry are described respectively in Section 4 and Section 5, with the re-evaluating results for both effects using the new EMG data set. In addition, a number of techniques that are possible to make the consistent results for both effects are suggested. In Section 6, the usefulness of MNF and MDF in the EMG pattern classification is proposed with the related works. The modified MNF and MDF in order to improve the robustness property for the classifying EMG signals are also presented. Lastly, the conclusion and future trends of using MNF and MDF to analyse EMG signals are

Frequency-domain or spectral-domain features are usually used in the assessing muscle fatigue and analysing MU recruitment (Oskoei & Hu, 2008). To transform the EMG signal in the time-domain to the frequency-domain, a Fourier transform of the autocorrelation function of the EMG signal is employed to provide the power spectrum (PS) or the power spectral density (PSD). Although PSD can be estimated by different methods, i.e. modern, parametric or model-based, the most commonly used PSD estimator in the EMG signal analysis is the Periodogram. It is defined as the square of absolute value of the Fourier transform of EMG signal divided by the signal length. Another stable and accurate PSD estimator is the autoregressive (AR) model (Zhang et al., 2010). Different kinds of statistical variables are applied to the PSD of EMG signal and the two popular used variables of PSD are mean and median. However, there are several possible statistical variables that can be applied to the PSD of EMG signal, such as summation or total, and peak value. Definitions

MNF is an average frequency which is calculated as the sum of product of the EMG power spectrum and the frequency divided by the total sum of the power spectrum (e.g. Oskoei & Hu, 2008; Phinyomark et al., 2012a). MNF has a similar definition as several features, i.e.

**2. Principle and theory of mean and median frequencies** 

of other statistical variables are presented in Section 2.2.

**2.1. The definition of mean and median frequencies** 

$$\text{MNF} = \sum\_{j=1}^{M} f\_j P\_j \left/ \sum\_{j=1}^{M} P\_j \right. \tag{1}$$

where *fj* is the frequency value of EMG power spectrum at the frequency bin *j*, *Pj* is the EMG power spectrum at the frequency bin *j*, and *M* is the length of frequency bin. In the analysis of EMG signal, *M* is usually defined as the next power of 2 from the length of EMG data in time-domain.

MDF is a frequency at which the EMG power spectrum is divided into two regions with equal amplitude (e.g. Oskoei & Hu, 2008; Phinyomark et al., 2012a). MDF is also defined as a half of the total power, or TTP (dividing the total power area into two equal parts). The definition of MDF is given by

$$\sum\_{j=1}^{MDF} P\_j = \sum\_{j=MDF}^{M} P\_j = \frac{1}{2} \sum\_{j=1}^{M} P\_j \,. \tag{2}$$

The behaviour of MNF and MDF is always similar. However, the performance of MNF in each of the applications is quite different compared to the performance of MDF, although both features are two kinds of averages in statistics. More details about the performance of both features are discussed in Section 3 to Section 6.

It should be noted that MNF is always slightly higher than MDF because of the skewed shape of EMG power spectrum (Knaflitz et al., 1990), whereas the variance of MNF is typically lower than that of MDF. In theory, the standard deviation of MDF is higher than that of MNF by a factor 1.253 (Balestra et al., 1988). However, the estimation of MDF is less affected by random noise, particularly in the case of noise located in the high frequency band of EMG power spectrum, and more affected by muscle fatigue (Stulen & De Luca, 1981).

#### **2.2. The relations between mean and median frequencies and other EMG frequency-domain features**

Other spectral variables that have been applied in the analysis of EMG signal are total power (TTP), mean power (MNP), peak frequency (PKF), the spectral moments (SM), frequency ratio (FR), power spectrum ratio (PSR), and variance of central frequency (VCF) (Phinyomark et al., 2012a). The definition of all variables is presented in the following.

1. TTP is an aggregate of EMG power spectrum (Phinyomark et al., 2012a). This feature is also defined as the energy and the zero spectral moment (SM0) (Du & Vuskovic, 2004). Its equation can be expressed as

Computational Intelligence in Electromyography Analysis – 198 A Perspective on Current Applications and Future Challenges

$$TTP = \sum\_{j=1}^{M} P\_j = \text{SMO} \ . \tag{3}$$

The Usefulness of Mean and Median Frequencies in Electromyography Analysis 199

<sup>2</sup> <sup>2</sup>

= − =− . (9)

where *f0* is defined as the value of PKF and *n* is the integral limit.

1

*j*

*j j*

1 *<sup>M</sup>*

be computed by the following equation

MNF and MDF patterns (Phinyomark et al., 2012a).

7. VCF is defined by using a number of the spectral moments (SM0-SM2) and MNF. It can

( )

TTP, MNP, and SM are frequency-domain features that extract the same information as time-domain features based on the energy information (Phinyomark et al., 2012a). Hence, the discriminant of TTP, MNP and SM in space has the similar pattern as the time-domain features based on the energy information, i.e. integrated EMG (IEMG), root mean square (RMS), mean absolute value (MAV), and variance of EMG (VAR). Due to the fact that muscle fatigue results in an increase of EMG signal amplitude, time-domain features based on energy information, i.e. IEMG, MAV and RMS, can track this behaviour. Thus, TTP, MNP and SM can also be used as an indicator of muscle fatigue, although EMG signal amplitude, itself, is rarely used to detect muscle fatigue. However, these features can be used in a combination with the spectral analysis i.e. MNF and MDF. On the other hand, all spectral features except PSR have the different discriminant patterns in feature space compared with MNF and MDF. In case of *n* = 20, the pattern of PSR is an inverse case of

*SM2 SM1 VCF P f MNF SM0 SM0 SM0* <sup>=</sup>

**3. Assessing the muscle fatigue using mean and median frequencies** 

fatigue monitoring during the performance of defined work (Petrofsky et al., 1982).

instantaneous frequency, wavelet analysis, fractal analysis, and also MNF and MDF.

The assessment of muscle fatigue with surface EMG signals can be applied in a wide class of applications, such as muscle fatigue during repeated cycling sprints (Hautier et al., 2000), muscle fatigue in children with cerebral palsy (Leunkeu et al., 2010), muscle fatigue during playing the PC games (Oskoei et al., 2008), and the low back pain in helicopter pilots (Balasubramanian et al., 2011). Several classical and modern signal processing techniques have been applied (Cifrek et al., 2009), such as the RMS, the zero-crossing rate (ZCR), the averaged

Muscle fatigue is generally defined as an activity induced loss of the ability to produce force with the muscle. Usually, the muscle fatigue is a result of prolonged or repetitive works (De Luca, 1984). It should be noted that the usual term "muscle fatigue" is generally meaning in fact "local muscle fatigue" (Chaffin, 1973). Undetected fatigue for a long-time can cause injury to the subject and is often irreversible. If an automated muscle fatigue detection system in wearable technology was feasible, it could be employed as an indicator to reduce the chances of work-place injury and aid sporting performance (Al-Mulla et al., 2012). Among a number of sources and techniques (Al-Mulla et al., 2011), e.g. acoustic-myography (AMG), mechano-myography (MMG), near-infrared spectroscopy (NIRS), sono-myography (SMG) and ultrasound, the EMG signal is used even more often and has several advantages, such as a non-invasiveness, an ability to monitor fatigue of a particular muscle and a real-time muscle

2. MNP is an average power of EMG power spectrum (Phinyomark et al., 2012a). It can be defined as

$$\text{MNP} = \sum\_{j=1}^{M} P\_j \left/ \boldsymbol{M} \right. \tag{4}$$

3. PKF is a frequency at which the maximum EMG power occurs (Phinyomark et al., 2012a). It can be expressed as

$$PKF = \max(P\_j), \quad j = 1, \dots, M \ . \tag{5}$$

4. SM is an alternative statistical analysis way to extract feature from the power spectrum of EMG signal. Normally, the first three moments (SM1-SM3) are employed as the EMG features (Du & Vuskovic, 2004). Their equations can be defined as

$$\text{SM1} = \sum\_{j=1}^{M} P\_j f\_j; \quad \text{SM2} = \sum\_{j=1}^{M} P\_j f\_j^2; \quad \text{SM3} = \sum\_{j=1}^{M} P\_j f\_j^3 \;. \tag{6}$$

5. FR is used to discriminate between relaxation and contraction of the muscle using a ratio between low- and high-frequency components of EMG signal (Han et al., 2000; Phinyomark et al., 2012a). The equation is defined as

$$FR = \sum\_{j=\text{LLC}}^{\text{ULC}} P\_j \left/ \sum\_{j=\text{LHC}}^{\text{LHC}} P\_j \right. \tag{7}$$

where *ULC* and *LLC* are respectively the upper- and the lower-cutoff frequency of lowfrequency band, and *UHC* and *LHC* are respectively the upper- and the lower-cutoff frequency of high-frequency band. The cutoff frequency between low- and high-frequencies can be defined by two ways: the experiment (Han et al., 2000) and the MNF value (Oskoei & Hu, 2006).

6. PSR is a ratio between the energy *P0* which is nearby the maximum value of EMG power spectrum and the energy *P* which is the whole energy of EMG power spectrum (Qingju & Zhizeng, 2006). It can be seen as an extended version of PKF and FR. The equation can be expressed as

$$PSR = \frac{P\_0}{P} = \sum\_{j=f\_0-n}^{f\_0+n} P\_j \left/ \sum\_{j=-\infty}^{\infty} P\_j \right. \tag{8}$$

where *f0* is defined as the value of PKF and *n* is the integral limit.

Computational Intelligence in Electromyography Analysis – 198 A Perspective on Current Applications and Future Challenges

defined as

Hu, 2006).

equation can be expressed as

2012a). It can be expressed as

1

2. MNP is an average power of EMG power spectrum (Phinyomark et al., 2012a). It can be

1

3. PKF is a frequency at which the maximum EMG power occurs (Phinyomark et al.,

4. SM is an alternative statistical analysis way to extract feature from the power spectrum of EMG signal. Normally, the first three moments (SM1-SM3) are employed as the EMG

11 1

*jj j SM1 P f SM2 P f SM3 P f* == =

5. FR is used to discriminate between relaxation and contraction of the muscle using a ratio between low- and high-frequency components of EMG signal (Han et al., 2000;

*ULC UHC*

where *ULC* and *LLC* are respectively the upper- and the lower-cutoff frequency of lowfrequency band, and *UHC* and *LHC* are respectively the upper- and the lower-cutoff frequency of high-frequency band. The cutoff frequency between low- and high-frequencies can be defined by two ways: the experiment (Han et al., 2000) and the MNF value (Oskoei &

6. PSR is a ratio between the energy *P0* which is nearby the maximum value of EMG power spectrum and the energy *P* which is the whole energy of EMG power spectrum (Qingju & Zhizeng, 2006). It can be seen as an extended version of PKF and FR. The

0

*PSR P P*

*f n*

0

*P*

*P*

0

*jf n j*

+ ∞

= − =−∞

*j j*

*j j j LLC j LHC FR P P* = =

*j j j j j j*

*MM M*

features (Du & Vuskovic, 2004). Their equations can be defined as

;

Phinyomark et al., 2012a). The equation is defined as

*M j j MNP P M* =

= = . (3)

<sup>=</sup> . (4)

max( ), =1, ..., *<sup>j</sup> PKF P j M* = . (5)

2 3

= = ;= . (6)

<sup>=</sup> , (7)

= = , (8)

*M j j TTP P SM0* =

7. VCF is defined by using a number of the spectral moments (SM0-SM2) and MNF. It can be computed by the following equation

$$\text{VCF} = \frac{1}{\text{SMO}} \sum\_{j=1}^{M} P\_j \left( f\_j - \text{MNF} \right)^2 = \frac{\text{SM2}}{\text{SMO}} - \left( \frac{\text{SM1}}{\text{SMO}} \right)^2 \,. \tag{9}$$

TTP, MNP, and SM are frequency-domain features that extract the same information as time-domain features based on the energy information (Phinyomark et al., 2012a). Hence, the discriminant of TTP, MNP and SM in space has the similar pattern as the time-domain features based on the energy information, i.e. integrated EMG (IEMG), root mean square (RMS), mean absolute value (MAV), and variance of EMG (VAR). Due to the fact that muscle fatigue results in an increase of EMG signal amplitude, time-domain features based on energy information, i.e. IEMG, MAV and RMS, can track this behaviour. Thus, TTP, MNP and SM can also be used as an indicator of muscle fatigue, although EMG signal amplitude, itself, is rarely used to detect muscle fatigue. However, these features can be used in a combination with the spectral analysis i.e. MNF and MDF. On the other hand, all spectral features except PSR have the different discriminant patterns in feature space compared with MNF and MDF. In case of *n* = 20, the pattern of PSR is an inverse case of MNF and MDF patterns (Phinyomark et al., 2012a).

### **3. Assessing the muscle fatigue using mean and median frequencies**

Muscle fatigue is generally defined as an activity induced loss of the ability to produce force with the muscle. Usually, the muscle fatigue is a result of prolonged or repetitive works (De Luca, 1984). It should be noted that the usual term "muscle fatigue" is generally meaning in fact "local muscle fatigue" (Chaffin, 1973). Undetected fatigue for a long-time can cause injury to the subject and is often irreversible. If an automated muscle fatigue detection system in wearable technology was feasible, it could be employed as an indicator to reduce the chances of work-place injury and aid sporting performance (Al-Mulla et al., 2012). Among a number of sources and techniques (Al-Mulla et al., 2011), e.g. acoustic-myography (AMG), mechano-myography (MMG), near-infrared spectroscopy (NIRS), sono-myography (SMG) and ultrasound, the EMG signal is used even more often and has several advantages, such as a non-invasiveness, an ability to monitor fatigue of a particular muscle and a real-time muscle fatigue monitoring during the performance of defined work (Petrofsky et al., 1982).

The assessment of muscle fatigue with surface EMG signals can be applied in a wide class of applications, such as muscle fatigue during repeated cycling sprints (Hautier et al., 2000), muscle fatigue in children with cerebral palsy (Leunkeu et al., 2010), muscle fatigue during playing the PC games (Oskoei et al., 2008), and the low back pain in helicopter pilots (Balasubramanian et al., 2011). Several classical and modern signal processing techniques have been applied (Cifrek et al., 2009), such as the RMS, the zero-crossing rate (ZCR), the averaged instantaneous frequency, wavelet analysis, fractal analysis, and also MNF and MDF.

Among such techniques, MNF and MDF so far have been hailed as the gold standard for muscle fatigue assessment with surface EMG signals due to the fact that muscle fatigue results in a downward shift of frequency spectrum of the EMG signal. Moreover, during the fatigue of muscle, several changes have been found, i.e. a relative decrease in signal power at high-frequency, a small increase in signal power at low-frequency, an increase in spectrum slope at high-frequency, and a decrease in spectrum slope at low-frequency (Petrofsky et al., 1982; Sato, 1982; Viitasalo & Komi, 1977). There are several possible reasons for the changes in the EMG signal, such as the modulation of recruitment firing rate, the grouping and slowing of CV, and synchronization of the signal (De Luca, 1979; Hermens et al., 1984; Viitasalo & Komi, 1977).

The Usefulness of Mean and Median Frequencies in Electromyography Analysis 201

100%MVC

60%MVC

15 24±3 BB 30 7kg F 15-450

19 19-73 VL 5 50%MVC F 5-1000

10 30.2±6.1 BB 10 10, 30, 50, 70%MVC 30 10-450

10 22.9±1.5 RF, VL, VM 30 50%MVC F 20-480


7 26±7 BB 40 20-30%MVC F 1-1000

10 29.4±4.8 BB 25 60%MVC C1 20-500

14 22-43 RF, VL, VM 20 100%MVC C2 15-4000

30 - RF, VL, VM 20 60%MVC 60 10-500

10 24±1.5 BB 75 70%MVC F 2-600

11 24±4 BB 5 40%MVC F -

10 24±2.8 BB 5 40%MVC 90 -

70, 80, 90%MVC

12 31.4±11.1 FDS, ECR - 10, 20, 30, 40, 50, 60,

**Table 1.** A survey of the experimental conditions in related works about muscle fatigue assessment with surface EMG signals using MNF and MDF in chronological order. Note that *N* is the number of subjects; ID is the inter-electrode distance (mm); RT is the recording time (s); Filter is the specification of filtering (Hz); MVC is maximum voluntary contraction; F is the EMG data is recorded until the subject cannot support the required force level; C1 is the EMG data is recorded until force is below 35%MVC; C2 is the EMG data is recorded until force is below 50%MVC; BR is brachioradialis; BB is biceps brachii; TA is tibialis anterior; RF is rectus femoris; VL is vastus lateralis; VM is vastus medialis; FDI is first dorsal interossrous; PS is paraspinal; FDS is flexor digitorum superficialis; ECR is extensor carpi radialis.

10 - PS - 40, 50, 60%MVC 60 -



F 25-1350

170

F -

10 23.2±2.3 BR 40 25, 40, 70%MVC F -

6 - TA - 50, 60, 70, 80%MVC 90-

Reference *N* Age Muscle ID Force levels RT Filter

9 30-40 BB - 20, 40, 60, 80,

10 - RF, VL - 20, 30, 40, 50,

Petrofsky & Lind

(1980b)

(1996)

(1996)

Potvin (1997)

(1999)

(1999)

(2001)

(2002)

(2003)

(2003)

Gerdle et al. (1990)

Merletti & Roy

Masuda et al.

Rainoldi et al.

Cifrek et al. (2000)

Bonato et al. (2001)

MacIsaac et al.

Allison & Fujiwara

Bilodeau et al.

Georgakis et al.

Clancy et al. (2005)

Ravier et al. (2005)

Zaman et al. (2011)

Soares et al. (2011)

Arnall et al. (2002)

Mannion & Dolan

Using MNF and MDF to detect muscle fatigue in static contractions is clearly known because during static contraction the EMG signals may be assumed to be stationary during short-time intervals (0.5-2s). On the other hand, in dynamic contractions, the EMG signal information has been changed as a function of time that cannot be analyzed by simply applying FFT and most recently EMG studies have been applied to the study of dynamic contraction. The instantaneous mean and median frequency (IMNF and IMDF) are introduced to fulfill the requirement (Roy et al., 1998) by using time-frequency or time-scale approaches, such as short-time Fourier transform (STFT) (Cifrek et al., 2000; Thongpanja et al., 2010, 2011), Wigner distribution (WD), Choi-Williams distribution (CWD) (Knaflitz & Bonato, 1999), time-varying autoregressive approach (TVAR) (Zhang et al., 2010), and continuous wavelet transform (CWT) (Karlsson et al., 2000).

Further, there are several ways to use IMNF and IMDF to detect muscle fatigue. For example, Georgakis et al. (2003) demonstrated that the performance of the average of IMNF and IMDF is better than the traditional MNF and MDF. On the other hand, a slope of the regression line that fits the maximum values of IMNF and IMDF during cyclic contractions is used as a fatigue index in Cifrek et al. (2000).

Many research works reported on the effectiveness of MNF and MDF applied to EMG signal as a mean of identifying muscle fatigue. The experimental conditions for several studies (based on literature published between 1980-2011) are summarized in Table 1. Most of the studies have been performed MNF and MDF to detect the muscle fatigue in primarily static muscle contraction but also in dynamic muscle contraction.

In Table 1, most of the studies recorded EMG data from 10 subjects and the volunteers between 20 and 30 years of age (young subjects) are the main target. However, in Masuda et al. (1999), age of the subjects is ranged from 19 to 73 years (both young and older subjects). EMG signals obtained from young and older subjects are quite different, as mentioned in Tavakolan et al. (2011) that the difference in classification accuracy obtained from the young and older subjects is approximately 7%. Although Kalra et al. (2012) found that MDF of EMG is not significantly impacted by age at 50-100%MVC of the BB muscle, the effect of age needs to be carefully considered in future research. In addition to the effect of age, the effect of gender is another factor that should be paid more an interest (Kalra et al., 2012).


Computational Intelligence in Electromyography Analysis – 200 A Perspective on Current Applications and Future Challenges

continuous wavelet transform (CWT) (Karlsson et al., 2000).

muscle contraction but also in dynamic muscle contraction.

is used as a fatigue index in Cifrek et al. (2000).

al., 1984; Viitasalo & Komi, 1977).

Among such techniques, MNF and MDF so far have been hailed as the gold standard for muscle fatigue assessment with surface EMG signals due to the fact that muscle fatigue results in a downward shift of frequency spectrum of the EMG signal. Moreover, during the fatigue of muscle, several changes have been found, i.e. a relative decrease in signal power at high-frequency, a small increase in signal power at low-frequency, an increase in spectrum slope at high-frequency, and a decrease in spectrum slope at low-frequency (Petrofsky et al., 1982; Sato, 1982; Viitasalo & Komi, 1977). There are several possible reasons for the changes in the EMG signal, such as the modulation of recruitment firing rate, the grouping and slowing of CV, and synchronization of the signal (De Luca, 1979; Hermens et

Using MNF and MDF to detect muscle fatigue in static contractions is clearly known because during static contraction the EMG signals may be assumed to be stationary during short-time intervals (0.5-2s). On the other hand, in dynamic contractions, the EMG signal information has been changed as a function of time that cannot be analyzed by simply applying FFT and most recently EMG studies have been applied to the study of dynamic contraction. The instantaneous mean and median frequency (IMNF and IMDF) are introduced to fulfill the requirement (Roy et al., 1998) by using time-frequency or time-scale approaches, such as short-time Fourier transform (STFT) (Cifrek et al., 2000; Thongpanja et al., 2010, 2011), Wigner distribution (WD), Choi-Williams distribution (CWD) (Knaflitz & Bonato, 1999), time-varying autoregressive approach (TVAR) (Zhang et al., 2010), and

Further, there are several ways to use IMNF and IMDF to detect muscle fatigue. For example, Georgakis et al. (2003) demonstrated that the performance of the average of IMNF and IMDF is better than the traditional MNF and MDF. On the other hand, a slope of the regression line that fits the maximum values of IMNF and IMDF during cyclic contractions

Many research works reported on the effectiveness of MNF and MDF applied to EMG signal as a mean of identifying muscle fatigue. The experimental conditions for several studies (based on literature published between 1980-2011) are summarized in Table 1. Most of the studies have been performed MNF and MDF to detect the muscle fatigue in primarily static

In Table 1, most of the studies recorded EMG data from 10 subjects and the volunteers between 20 and 30 years of age (young subjects) are the main target. However, in Masuda et al. (1999), age of the subjects is ranged from 19 to 73 years (both young and older subjects). EMG signals obtained from young and older subjects are quite different, as mentioned in Tavakolan et al. (2011) that the difference in classification accuracy obtained from the young and older subjects is approximately 7%. Although Kalra et al. (2012) found that MDF of EMG is not significantly impacted by age at 50-100%MVC of the BB muscle, the effect of age needs to be carefully considered in future research. In addition to the effect of age, the effect

of gender is another factor that should be paid more an interest (Kalra et al., 2012).

**Table 1.** A survey of the experimental conditions in related works about muscle fatigue assessment with surface EMG signals using MNF and MDF in chronological order. Note that *N* is the number of subjects; ID is the inter-electrode distance (mm); RT is the recording time (s); Filter is the specification of filtering (Hz); MVC is maximum voluntary contraction; F is the EMG data is recorded until the subject cannot support the required force level; C1 is the EMG data is recorded until force is below 35%MVC; C2 is the EMG data is recorded until force is below 50%MVC; BR is brachioradialis; BB is biceps brachii; TA is tibialis anterior; RF is rectus femoris; VL is vastus lateralis; VM is vastus medialis; FDI is first dorsal interossrous; PS is paraspinal; FDS is flexor digitorum superficialis; ECR is extensor carpi radialis.

The next interested factor in Table 1 is the recording time. Because in the analysis of muscle fatigue, the EMG signals recorded during the fatigue of muscle are needed. Most of the studies used a level of force as the threshold to finish the recording. In other words, the EMG data have been recorded until the subject cannot maintain the required force level. However, several studies define the specific recording times that range from 30s to 170s.

The Usefulness of Mean and Median Frequencies in Electromyography Analysis 203

3 - 1

3-5 0.2-2000 1,2

1-2 - 2

3 16-800 1

5 0.1-3000 1

8 20-450 2

5 20-500 3

7 - RF,VL,VM - - - - 1

8 22-52 FCR 40 5-100%MVC 3 - 1

100%MVC

5 32.5±8.2 BB - 0.25, 0.5, 1, 2, 3kg 10 - 2

26 22.6±6.4 FDI 10 20, 80%MVC 3-5 30-350 1

19 18-32 FL,CS 15 20, 40, 60, 80%MA 3 3-520 2

6 20-40 BB - 1-10Nm 8.2 - 2

9 30-40 BB,ED 35 30, 50, 70, 90%MVC 6 1-1000 1

12 26.3±2.5 BB 6 0-80%MVC 5 < 520 2

14 36±8 TZ 30 0-100%MVC 10-15 5-500 2

100%MVC

80%MVC

10 30.2±6.1 BB 10 10, 30, 50, 70%MVC 30 10-450 3

60%MVC

70%MVC

100%MVC

40, 50, 80%MVC

Reference *N* Age Muscle ID Force levels RT Filter CF

10 23.2±2.3 BR 40 10, 20, 40, 60, 80,

4 21-24 BB,BR,BL 20 5, 10, 15, 20, 25, 30,

9 30-40 BB - 20, 40, 60, 80,

14 30.2±7.8 TB,AN 6 10, 20, 40, 60,

12 31.1±10 BB 40 20, 30, 40, 50,

94 5-69 BB 10 10, 30, 50, 70,

12 24.8±2.8 BB,BR,TB 10 10, 20, 30, 40, 50, 60,

**Table 2.** A survey of the experimental conditions in related works about the effect of muscle force on MNF and MDF in chronological order. Note that CF is one of three conflicting cases for muscle force effect; MA is maximum amplitude; FCR is flexor carpi radialis; BL is brachialis; FL is frontalis; CS is corrugator supercilii; ED is extensor digitorum; TZ is trapezius; TB is triceps brachii; AN is anconeus.

Viitasalo & Komi

Petrofsky & Lind

Petrofsky & Lind

(1978)

(1980a)

(1980b)

Hagberg & Ericsson (1982)

Muro et al. (1982)

Merletti et al.

Van Boxtel & Schomaker (1984)

Gander & Hudgins (1985)

Inbar et al. (1986)

Hagberg & Hagberg (1989)

Gerdle et al. (1990)

Bilodeau et al.

Rainoldi et al.

Cechetto et al.

Doheny et al.

Kaplanis et al.

(1987)

(1991)

(1999)

(2001)

(2008)

(2009)

Moritani & Muro

(1984)

Other factors are varied, such as the inter-electrode distance (5-75 mm), the levels of force (10-100%MVC), and the specification of filtering (1-1350 Hz). However, most of the studies paid more an interest to the study of biceps brachii muscle. The evaluating performance between each pair of the methods and the muscles should be done in future study.
