**1. Introduction**

Computational Intelligence in Electromyography Analysis – 96 A Perspective on Current Applications and Future Challenges

139-159. Champaign, IL: Human Kinetics Publishers.

Printing, Calgary).

Sport Sci Rev 29(1):37-41.

running.J Biom 36: 569-575.

Thorofare, NJ: Slack 353-411.

21 (6):805-810.

York:Wyley, 1990.

1994;4:15-26.

Clin Biomech (2004) Jan;19(1):71-7.

Nigg BM (1983). External force measurements with sport shoes and playing surfaces; in Nigg Kerr, Biomechanical aspects of sport shoes and playing surfaces 11-23 (University

Nigg BM, Bahlsen HA, Denoth J, Luethi SM, Stacoff A (1986). Factors influencing kinetic and kinematic variables in tuning. In: Biomechanics of running shoes. BM Nigg (ed.). Pp

Nigg BM, Wakeling JM (2001). Impact forces and muscle tuning: a new paradigm. Exerc

Nigg BM, Mundeermann A, Stefanyshyn DJ, Cole G, Stergiou P, Miller J (2003).The effect of material characteristics of shoe soles on muscle activation and energy aspects during

Perry J (1992). Gait analysis sistems in gait analysis: normal and pathological function.

Nordin M and Frankel VH (2004). Biomecanica de la rodilla. In: Biomecanica basica del

O'Connor KM, Hamill J (2005).The role of selected extrinsic foot muscles during running.

Ramiro J, Ferranids R, Sánchez J, Alepuz R, Latorre P, Dejoz R, Candela F (1988). Evaluación de la técnica del calzado deportivo. Archivos Medicina del Deporte V (18): 161-168. Reber L, Perry J, Pink M (1993). Muscular control of the ankle in running. Am J Sports Med

Segesser B, Nigg BM (1993). Orthopedic and biomechanical concepts of sports shoe

Shorteen MR (1993). The energetics of running and running shoes. J Biom 26 (1): 41-51. Slocum DB, James SL (1968). Biomechanics of running. JAMA. 1968 Sep 9;205(11):721-8. Staude G, Wolf W (1999). Objective motor response onset detection in surface myoelectric

Testut L, Latarjet A (1971). Tratado de Anatomía Humana. Barcelona: Salvat editores.

Wakeling JM, Von Tscharner V, Nigg BM, Stergiou P (2001). Muscle activity in the leg is tuned in response to ground reaction forces. J Appl Physiol Sep;91(3):1307-17. Wakeling JM, Pascual S, Nigg B M (2002). Altering muscle activity in lower extremities by

Winter DA (1979). Mechanical work, energy and power. In: Biomechanics of human

Winter DA (1990). Biomechanical and motor control of human movement, 2nd edn. New

Winter DA, Fugelvand AJ, & Archer SE (1994). Crosstalk in surface electromyography: theoretical and practical estimates. Journal of Electromyography and Kynesiology

Wickiewiz TL, Roland R, Perry L, Powell BS, Edgerton R (1983). Muscle architecture of the

human lower limb. Clinical Ortopaedics and Related Research 179: 275-283.

Vaughan, CL (1984). Biomechanics of running. Crit Rev Biomed Eng 12:1-48

running with different shoes. Med. Sci. Sports Exerc. 34 (9): 1529-1532.

sistema musculoesqueletico. McGraw Hill/Inteaméricana de Espana, S.A.U.

Novacheck TF (1998). The biomechanics of running. Gait & Posture 7: 77-95.

SENIAM (1999). European recommendations for surface electromyography.

construction Sportverletz Sportschaden 7 (4) 150-162.

signals. Medical Engineering & Physics 21:449-467.

movement. Pp 84-107. Toronto: John Wiley and Sons.

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 perspectives we had been working with.

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

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

© 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.

not be the best way to pre-process the EMG signal. Other current concern, in EMG signal pre-processing, is about the use of the total signal against evaluation only the burst-time segments of the signal. Those concerns are explained and analyzed along this chapter. In an epistemological language, we take a more critic look into the EMG signal processing. We hope the reader also to have the same look, not only into the results and conclusions, but also, into methods and thoughts, since the intention herein is not to bring an irrefutable true, but the real intention is to discuss and point out valuable arguments for the reader in order to he/she thinks about it by himself or herself, and apply it properly.

Influence of Different Strategies of Treatment Muscle Contraction

and Relaxation Phases on EMG Signal Processing and Analysis During Cyclic Exercise 99

signal stationary is a complicated thing to deal with, which leads us to use a wavelet transform, more appropriate to cyclic activities as cycling and running for example [6-9].

Independently of technique used we should get some variable from this analyses to compare, relate or make our considerations, in this case, the most common variable toke from frequency domain is the median frequency, representative of fatigue aspect in the muscular activity from decrease of conduct fibers velocity, is exactly the point that divided the spectrum in two equal parts and gives us a good representation of reduction in the force

Time domain is used when the intention is to achieve the contractibility of the muscle, meaning that as stronger the signal the most number of motor units are been activated. The most common variable used inside this domain is the Root Mean Square (RMS) [9]. To get this variable some procedures are required, like the filtration, rectification and smoothing, those will be better explained later. Just like the frequency domain, a correct time window is necessary and follows the frequency domain also when talking about the use of wavelet

Now you already know about how the domains work and how to use them for different analysis depending on the applications necessity. During the subchapter "Analysis of the EMG signal" we hope it became clear that we have some procedures until the real signal is accessed, especially without noises. The raw signal can already give us some information, like the muscle innervations or even the change in the signal size. Depending of the intention, these qualitative variables can be very useful. An easy and good way to simple control some noises when there is no intention of further computer treatment to remove it, is to be sure to have a good baseline, meaning that the line that should appear at the EMG signal must be as close as it can to zero when the muscle with the electrode connected is not in contraction. That doesn't mean that when the muscle starts to contract the signal that will appear will only be from the muscle activity, especially in dynamic contractions. There are three main differences in noises on static contractions and dynamic ones, they are: the nonstationarity of the signal for the constant contraction and relaxing of the muscle, the change of the electrode distance relative to the origin of the action potential and the changes in the

A better way to understand what a noise is, is looking at it, the figure 1 under is an EMG signal with a closer look in the burst moment. Notice that the areas surrounded with black circles have a peculiar difference, it has a horizontal straight shaped line, which means that those parts don't have a corresponding negative part, and so, it is considered a noise. Of course in this same image you can find some more of those, not only the surrounded ones,

When the signal appears to us in the computer screen those details are impossible to see without a zoom look. So, lets talk now about how the treatments can influence in the signal

but the intention here is only to show how a noise appears inside an EMG signal.

produced.

transform.

value.

**2.3. Treating the EMG signal** 

conductivity of the tissues properties [10].
