**1. Introduction**

20 Will-be-set-by-IN-TECH

[27] Teh C. H., Chin, R. T. (1988) On image analysis by the methods of moments. *I*EEE Trans.

[28] Trussell H.J. & S. Fogel,(1992) Identification and Restoration of Spatially Variant Motion

[29] Tull D. L & Katsaggelos A.K.,(1996) Iterative Restoration of Fast Moving Objects in Dy-

[30] Wang G., Wei Y. & Qiao S. (2004) *Generalized Inverses: Theory and Computations*, Science

Blurs in Sequential Images, *I*EEE Trans. Image Proc., 1, 123-126.

namic Images Sequences, *Optical Engineering*, 35(12), 3460-3469.

Pattern Anal. Machine Intell, 10, 496-513.

Press, Beijing/New York.

This chapter presents the anatomy of Electromyography (EMG) signal, measurement, analysis, and it's processing. EMG is the detection of the electrical activity associated with muscle contraction. It is obtained by measurement of the electrical activity of a muscle during contraction. EMG signals are directly linked to the desire of movement of the person.

Robot arms are versatile tools found in a wide range of applications. While the user moves his arm, (EMG) activity is recorded from selected muscles, using surface EMG electrodes. By a decoding procedure the muscular activity is transformed to kinematic variables that are used to control the robot arm. EMG signals have been used as control signals for robotics devices in the past. EMG signals, which are measured at the skin surface, are the electrical manifestations of the activity of muscles. It provides an important access to the human neuromuscular system. It has been well recognized as an effective tool to generate control commands for prosthetic devices and human-assisting manipulators. Up to the present, a number of EMG-based human interfaces have been proposed as a means for elderly people and the disabled to control powered prosthetic limbs, wheelchairs, teleoperated robots, and so on. The core part of these human–robot interfaces is a pattern classification process, where motions or intentions of motions are classified according to features extracted from EMG signals. Commands for device control are then generated from the classified motions (Bu et al., 2009).

It has been proposed that the EMG signals from the body's intact musculature can be used to identify motion commands for the control of an externally powered prosthesis. Information extracted from EMG signals, represented in a feature vector, is chosen to minimize the control error. In order to achieve this, a feature set must be chosen which maximally separates the desired output classes. The extraction of accurate features from the EMG signals is the main kernel of classification systems and is essential to the motion command identification (Park &Lee, 1998).

© 2012 Al-Faiz and Miry, 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 Al-Faiz and Miry, 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.
