**5. References**


Gavilanes MB, Goiriena de Gandarias JJ (2004). Muscle activity in shod and barefoot healthy young subjects during walking. In : International proceedings of XVth Congress of the International Society of Electrophysiology & Kinesiology. Boston U.S.A. 2004:105

Computational Intelligence in Electromyography Analysis – 94 A Perspective on Current Applications and Future Challenges

partially this work through grant CSD2009-00067.

Authors thank the volunteers who participated in the experiments carried out for the present work. Thanks also to J. de la Cruz (Department of Applied Economy, University of Basque Country, Spain), F. Ainz (Department of Physiology, University of Basque Country, Spain), and to J. Bilbao (Department of Statistics, University of Basque Country, Spain) for their participation in the analysis of the data; and to S. Rainieri (Food Research Division,

This study was supported by the Foundation *Jesús de Gangoiti Barrera*. G.A.G. was supported by a European Marie Curie Post-doctoral Fellowship (ADCOMP project; Contract MEIF-CT-2006-025056). The CONSOLIDER INGENIO 2010 must be acknowledged for supporting

Arsenault AB, Winter DA, Marteniuk RG (1987). Is there a 'normal ' profile of EMG activity

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**Acknowledgement** 

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**Chapter 5** 

© 2012 Altimari et al., 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 Altimari et al., 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.

**Influence of Different Strategies of Treatment** 

Thiago Ferreira Dias Kanthack, Antonio Carlos de Moraes and Taufik Abrão

For a long time we work with muscular activity, trying to answer questions related to fatigue, muscle activity and other issues related to neuromuscular system. In this way we started to use the electromyography (EMG) as a tool to achieve better results in our studies, since it appeared to us as a truthful method to access the muscle activity inside a lot of

In this chapter we will try to bring some research results that we found on the GEPESINE laboratory in the last couple of years about regarding the EMG analysis. Firstly there are relevant issues that arise during the use of EMG as a tool in others works. It is not hard to find studies that use EMG signal as a way to measure the muscle activity [1-3], muscle fatigue [4] and also in studies involving healthy issues [5]. Most of those studies try to access the activity or fatigue slope of the muscle during some motor task, mostly trying to access performance or just to categorize an activity according to the muscle(s) accessed. The real problem is that most of those studies use isometric movements or even isokinetic, leaving a remarkable problem for the researchers who decide to work with dynamic contractions,

We have decided to take a different look to the process on how to treat the EMG signal and how to analyze it. For instance, in order to have a more trustful signal, founds in literature recommend filtering, smoothing the raw and also rectifying the signal, which the last step does not affect the signal power. However, the filtered root mean square (RMS) signal could

once the available protocols are most based on and suitable isometric studies.

**Muscle Contraction and Relaxation Phases** 

**on EMG Signal Processing and Analysis** 

Leandro Ricardo Altimari, José Luiz Dantas, Marcelo Bigliassi,

**During Cyclic Exercise** 

http://dx.doi.org/10.5772/50599

perspectives we had been working with.

**1. Introduction** 

Additional information is available at the end of the chapter

