**7.1 Filters—bandpass**

Not only the amplitude of the sEMG signal depends on the site of acquisition but also the frequencies vary with it. Other factors depend on the subject physical health as the sEMG signal of athletes has higher amplitude and frequencies as compared with the normal subject [8]. The recommended cutoff frequencies setting for the bandpass filter are from 10 to 500 Hz [7]. However, in some cases, a tight band of 10–250 Hz may be used.

**Figure 8.**

*The signal processing blocks in a typical sEMG acquisition system.*

#### **7.2 Rectification**

In electrical, rectification is referred to a process that converts the alternating current (AC) to direct current (DC). In signal processing, the rectification is referred to convert the negative values of a signal into positive. In other words, the rectification is similar to take the absolute value of the signal. **Figure 5a** shows the signal of sEMG obtained during the contraction of bicep muscle. The rectified sEMG signal can be seen in **Figure 5b**. The signal in the black color (in **Figure 5b**) is obtained after the smoothing filter which will be discussed in the next section.

#### **7.3 Smoothing filters**

The sEMG signal is random and cannot be reproduced again with same amplitude, frequency, and shape. Therefore, sEMG undergoes signal smoothing techniques to minimize the effect of the non-reproducible part of the signal. Mostly, following techniques are used for smoothing the sEMG signal and one can select anyone of the techniques depending on the system requirements.

#### *7.3.1 Root mean square (RMS)*

The root mean square or RMS is the most commonly used technique among the signal processing community for the smoothing of signal. The RMS calculation is based on the square root and the mean power of the signal as shown in the Eq. (6).

$$RMS = \sqrt{\frac{\sum\_{i=1}^{n} \mathcal{X}\_i^2}{N}} \tag{6}$$

#### *7.3.2 Moving average filter (MA)*

As the name depicts, the moving average (MA) filter takes average of the samples as it moves forward. In other words, a window is set defined for a specific number of samples, then the data in the window are averaged by the sliding window technique resulting in the smoothing of the signal. The MA filter can be applied on the sEMG signal using Eq. (7).

$$\mathbb{E}\left[\mathbf{y}\middle|\bar{i}\right] = \frac{\mathbf{1}}{M} \sum\_{j=0}^{M-1} \mathbb{X}\left[\bar{i} + \bar{j}\right] \tag{7}$$

**21**

**Author details**

Sarmad Shams1

\* †

2 Ziauddin University, Karachi, Pakistan

† These authors contributed equally.

provided the original work is properly cited.

, Muhammad Asif2† and Samreen Hussain3

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

have developed a number of techniques to monitor the behavior of human mechanics using 3D cameras, motion sensors, reflector markers but none of the technique gave the essential analysis which can be obtained through EMG. Therefore, this chapter covers the essential knowledge required to build a knowledge base for the reader for

the better understanding of the Biomechanics of human body.

1 Sir Syed University of Engineering and Technology, Karachi, Pakistan

3 Begum Nusrat Bhutto Women University, Sukkur, Pakistan

\*Address all correspondence to: sarmadshams@yahoo.com

*Muscle Mechanics and Electromyography DOI: http://dx.doi.org/10.5772/intechopen.93282*

#### *7.3.3 Low pass filter*

Another common technique for smoothing of the sEMG signal is to use a low pass filter that blocks all the higher frequencies of the signal. For this purpose, usually, a low pass filter with a cutoff frequency between 2 and 5 Hz is used.

#### **8. Conclusions**

The biomechanics is basically divided into four major areas: kinetics, kinematics, anthropometery, and electromyography. To understand the biomechanics, one must have clear understanding of all the four major areas especially the EMG. Since, the main application of the biomechanics is to observe the problem and improve the diagnosis and fixation of any disease or lacking identified during the analysis. Researchers

#### *Muscle Mechanics and Electromyography DOI: http://dx.doi.org/10.5772/intechopen.93282*

*Recent Advances in Biomechanics*

In electrical, rectification is referred to a process that converts the alternating current (AC) to direct current (DC). In signal processing, the rectification is referred to convert the negative values of a signal into positive. In other words, the rectification is similar to take the absolute value of the signal. **Figure 5a** shows the signal of sEMG obtained during the contraction of bicep muscle. The rectified sEMG signal can be seen in **Figure 5b**. The signal in the black color (in **Figure 5b**) is obtained after the smoothing filter which will be discussed in the next section.

The sEMG signal is random and cannot be reproduced again with same amplitude, frequency, and shape. Therefore, sEMG undergoes signal smoothing techniques to minimize the effect of the non-reproducible part of the signal. Mostly, following techniques are used for smoothing the sEMG signal and one can select

The root mean square or RMS is the most commonly used technique among the signal processing community for the smoothing of signal. The RMS calculation is based on the square root and the mean power of the signal as shown in the Eq. (6).

*RMS*

As the name depicts, the moving average (MA) filter takes average of the samples as it moves forward. In other words, a window is set defined for a specific number of samples, then the data in the window are averaged by the sliding window technique resulting in the smoothing of the signal. The MA filter can be applied on

> [ ] [ ] 0 <sup>1</sup> 1 *<sup>M</sup> j yi xi j <sup>M</sup>* - =

Another common technique for smoothing of the sEMG signal is to use a low pass filter that blocks all the higher frequencies of the signal. For this purpose, usu-

The biomechanics is basically divided into four major areas: kinetics, kinematics, anthropometery, and electromyography. To understand the biomechanics, one must have clear understanding of all the four major areas especially the EMG. Since, the main application of the biomechanics is to observe the problem and improve the diagnosis and fixation of any disease or lacking identified during the analysis. Researchers

ally, a low pass filter with a cutoff frequency between 2 and 5 Hz is used.

2 1 *n <sup>i</sup> <sup>i</sup> x*

<sup>=</sup> <sup>=</sup> å (6)

= + å (7)

*N*

anyone of the techniques depending on the system requirements.

**7.2 Rectification**

**7.3 Smoothing filters**

*7.3.1 Root mean square (RMS)*

*7.3.2 Moving average filter (MA)*

the sEMG signal using Eq. (7).

*7.3.3 Low pass filter*

**8. Conclusions**

**20**

have developed a number of techniques to monitor the behavior of human mechanics using 3D cameras, motion sensors, reflector markers but none of the technique gave the essential analysis which can be obtained through EMG. Therefore, this chapter covers the essential knowledge required to build a knowledge base for the reader for the better understanding of the Biomechanics of human body.
