**6. Future directions**

Computational Intelligence in Electromyography Analysis – 112 A Perspective on Current Applications and Future Challenges

**5. Conclusions** 

be cut.

test, no differences were found between methods (p>0.05).

The figure 14 present us the MF comparison between different kinds of analyze (all signal phase and contraction phase) respectively, among muscles: RF, VM and VL in the Wingate

The results presented above show us that there were no significant difference between the two analyzes. Probably, these results were found because despite the silence moment has power gradient, the amount of lost energy is not enough to change the EMG signal parameters when the whole signal is analyzed. This result should not be transferred to others activity like the golf or to a martial art kick or punch for example due to the different characteristics, where in this sports the motor activity should be analyzed per complete, because during the whole time there is a contraction, so there is a signal amplitude [21-22].

We concluded that, although exist many orientations and recommendations to use and apply the electromyography method, sometimes these components can be a path too complex to understand and to respect with closed eyes. In a considerable perspective of study we were able to show with a model of exercise in high intensity, which was capable to produce a lot of noises and variations on the signal, that different methods of process to achieve the muscular activity do not change the final result if used the complete signal or just the burst parts, or still using all sequence of treatment with filtration, rectification and smoothing in many combinations of analyses. Moreover, should be noted that only filtration was sufficient to improve the quality of EMG signal, making us think in keeping the use at least the filtration in electromyography analyses, still this procedure is used to at least maintain the signal inside the muscle activity range, so, it should not be took out just because no significant differences were founded, we have to consider all the process, as said before, like the devices used and the investigator experience. These outcomes show us that we have remained with a critical knowledge to many things and test the main recommendations to use some techniques. In order to make those results clearer and give us more confidence when use the treatments in EMG analyzes. Some studies creating different noises in computer should be made. This way we can be more secure about the removing of noises, securing that the absence of difference is not because a good pre-acquisition was made, securing not enough noises to

These results and conclusion takes in consideration only cyclic exercise with the intensity used in the studies. Exercises such as isometric or acyclic have different signal waves and so, could have different results to the same treatments. Also, exercises with lower load could change mainly the results in the Burst + Silence (Second Study) results, once that a task such as 10 km in low intensity, would be realized with less intense movements, creating not only

Still, a more accuracy statistic method could be used, such as The Smallest Worthwhile Change [23], capable to find minimal and almost invisible differences between different methods, that can contribute with good perspective to sports domain when obscure changes

different power signals but also different silence and burst time duration.

Although, we should now take the conclusions and think in considerable applicability with our outcomes, we should remain our critical thinking about the theme, about our limitations and keep our considerations related just to our results in this study with these methods and these subjects. We expect that our findings encourage new experiences inside a positive vision in his complete trend to refute ours dogmas and explain in a better way how we should use and respect the recommendations and orientations to electromyography application in human studies, involving different kind of exercise in several intensities and oscillating between isometric and isotonic conditions, testing many aspects that stays around electromyography process and show to the scientific world a great amount of specificities to use this technique taking into account these variables capable to confuse and change the signal with noises, underestimating or overestimating the final value.

Such design, as showed on this chapter should be applied to others tasks with the same characteristics, like running or any other cyclic exercises that has different muscles involved with different activation ranges and other kinds of possible noises, and silence and burst times. Also, different data process should be tested for spectral analyses, like Wavelet families and Fast Fourier Transform (FFT) for different exercises modalities, until it comes to conclusion about the correct use of this technique involving the correct results achievement, signal process and interpretation.
