**1. Introduction**

Computational Intelligence in Electromyography Analysis – 336 A Perspective on Current Applications and Future Challenges

Yoshikawa, M., Mikawa, M., & Tanaka, K. (2009). Real-Time Hand Motion Classification Using EMG Signals with Support Vector Machines, *The IEICE Transactions on* 

*information and systems (Japanese edition)*, Vol.J92-D, No.1, (2009), pp.93-103

Many with disabilities have some difficulties in integrating into society due to impossibility or to restriction in performing simple tasks of day-to-day. This situation is gradually changing by virtue of technological development in the biomedical instrumentation area in respect of human rehabilitation and especially with in the development of assistive technology managed by computational intelligence (computing algorithms and learning machines using techniques as fuzzy logic, artificial neural networks, genetic algorithms, support vector machines, among others). Scientific researches in this area are allowing the development of several mechanisms to improve the life quality of people with special needs, making them more independent and more likely to real social and economical integration.

It's possible to cite, for example, research related to robotic prosthesis. The development of system managed by myoelectric signals (MES) with the intention to mimic the human arm movement, is far from perfect, making the subject of many researches (Ajiboye & Weir, 2005; Chan et al., 2000; Englehart & Hudgins, 2003; Favieiro & Balbinot, 2011; Favieiro et al., 2011; Hincapie & Kirsch, 2009; Hudgins et al., 1991; Hudgins et al., 1994; Jacobsen et al., 1982; Katutoshi et al., 1992; Khushaba et al., 2010; Momen et al., 2007; Park & Meek, 1995). These researches are mainly being conducted in able-bodies subjects to verify the feasibility and performance of different algorithms for pattern recognition using EMG signals from the forearm muscles. In these studies are usually employed a high number of electrode pairs, ranging from 4 to 12. Using classification patterns techniques such as LDA, fuzzy logic, among others, was found high accuracies (>90%) for the classification of different moves ranging from four to ten. Develop a robotic prosthesis as similar as possible to the human arm is not a simple task. There are great difficulties both in the area of distinguish the

© 2012 Winkler Favieiro and Balbinot, licensee InTech. This is an open access chapter 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, provided the original work is properly cited. © 2012 Winkler Favieiro and Balbinot, licensee InTech. This is a paper 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, provided the original work is properly cited.

various degrees of freedom that the arm can have as developing a robotic prosthesis that can accomplish or replicate all these movements.

Proposal of a Neuro Fuzzy System for Myoelectric Signal Analysis from Hand-Arm Segment 339

Typically a fuzzy system incorporates a rule base, membership functions and an inference procedure and has been presenting success in systems with applications in the presence of ambiguous elements (Begg et al., 2008; Zadeh et al., 2004). Systems combining neural

• computational models based on biological models, such as the use of neural networks

This chapter briefly presents the fuzzy techniques, adaptive algorithms, neuro-fuzzy and

A fuzzy set is defined as a set or collection of elements with membership values between 0 and 1. Therefore, the transition between belonging or not belonging to the set is gradual and is characterized by its fuzzy Membership Function (MF) that is used to describe the fuzzy membership value given to fuzzy set elements (Begg et al., 2008) enabling the fuzzy set model linguist expression used in everyday life, such as, "the rms value of the masseter myoelectric signal is medium high". For these reasons, the fuzzy sets theory is very efficient when dealing

Therefore, a fuzzy set not-empty Z in a given space **X** (���), is the set represented by

since �� a membership function of an specified fuzzy set. This function indicates for each element ����� its membership degree to the fuzzy set *Z* between three possibilities

• ��(�) = 1 means the full membership of element *x* to the fuzzy set Z, in others words,

• ��(�) = 0 means the lack of membership of element *x* to the fuzzy set Z, in others

A membership function (MF) is a curve that defines how a point in the input space is mapped into a membership degree between 0 and 1 (Dubois, 1980). Typically a MF is

• 0<��(�) < 1 means a partial membership of element *x* to the fuzzy set Z.

� = ���, ��(�)�� ���� (1)

����� � �0,1� (2)

with concepts of ambiguity (Zadeh, 1992) and allows its use in several applications.

networks with fuzzy systems usually have the following characteristics (Jang, 1997):

• human knowledge presented in the form of rules, for example, if-then;

• optimization techniques, such as the use of a hybrid technique;

• numerical computation instead of symbolic computation.

for pattern recognition;

**2.1. Fuzzy logic** 

equations (1) e (2):

(Rutkowski, 2005):

words, ���;

*2.1.1. Standard forms of membership functions* 

���;

• construction of a model with data sample;

data clustering used in the present research.

Briefly, the myoelectric signal is the bio-signal muscle control of the human body which contains the information of the user's intent to contract a muscle and, therefore, perform a certain movement. Studies have shown that amputees are able to repeatedly generate certain standard myoelectric signals in front of intention to carry out a particular movement. It makes the use of such signal highly advantageous, because the control of a robotic prosthesis can be accomplished according to user's intention to perform a specified movement. Furthermore, detection of the myoelectric signal can be obtained noninvasively through surface electrodes. Although the distress signal has low amplitude (mV range) is sufficient for its analysis and surface electrodes are far more hygienic and convenient as the removal, insertion and sterilization can be accomplished by the user.

Therefore, it is possible to distinguish certain muscle movements while processing the electrical parameters of the myoelectric signal both in time domain and frequency domain. With the characterized movements is possible to control a robotic prosthesis that aims to replicate, the best possible, the movements of a human arm. Considering that premise, this research aims to study and develop a system that uses myoelectric signals, acquired by surface electrodes, to characterize certain movements of the human arm, allowing studies between man and machine with adequate precision for future enabling the actual replacement of an amputee limb with a robotic prosthesis suitable and intuitively controlled through the remaining muscle signals. To recognize certain hand-arm segment movements, was developed an algorithm for pattern recognition technique based on neuro-*fuzzy*, representing the core of this research. This algorithm has as input the preprocessed myoelectric signal, to disclosed specific characteristics of the signal, and as output the performed movement.

The present research was also preoccupy in not only distinguish certain simple movements of the human arm, but also characterize complex movements that combine several degrees of freedom, making this study more closely to the reality, in which more degrees of freedom represents an improve in the life quality of people with special needs, making them more likely to real integration in the society.
