**2.2. Time and frequency domain**

Talk about frequency domain is talk about: how many times a event occurs in a time space, in this way, to use this component we need transform the signal in different points presented in a frequency spectrum capable to show us the energy of cue obtained in the determined muscle. This energy in the most part of the time appears represented in some bands, where your intensity and duration has more amplitude. To find and use the spectrum, we must find a source that gives us the possibility to produce this figure, when sometimes the Fast Fourier Transform proves as algorithm in a simple calculus to find discrete signals. A series of recommendations are proposed to this technique, since the establishment of sample number, duration intervals, window apply and many aspects as 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 produced.

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 transform.
