**5. Conclusions**

The proposed system was designed to use a limited amount of up to 8 channels of the myoelectric signal acquisition and with the assistance of a more robust artificial intelligence technique was able to verify the validity of this system in terms of performance in the characterization of 11 distinct movements, including 5 complex movements. In tests, the mean peak signal with a maximum voluntary contraction (MVC) appeared at least four times greater than the average peak signal at a time of muscle relaxation. Thus, was possible determine a level ranging from 30 to 50% of MVC to differentiate a time of muscle contraction, representing a movement. With the windowing signal occurs at the instant when a movement occurs, is possible to obtain the rms value for each of the channels 8 and to use these values as input to a neuro fuzzy network with one output an up to 8 inputs. This network aims to characterize the movements that are being executed. The network is adapted in accordance with supervised training, to evaluate system performance over time. As can be seen on the results, some movements have achieved a lower hit rate, this may occur due to poor signal quality, user error, and the quantity of motion that was presented to the neuro fuzzy network, since most of the errors were caused by similar movements, or the differentiation of compound movements with their simple movements, which have a response very similar in rms value, causing the network to get confused. The average accuracy obtained was 65% of hit rate to 11 distinct movements in tests of long duration, about three hours.

Also, it's important to notice that the vast majority of studies in the area of recognition of hand-movements of the arm segment are based on the classification of simple movements, not taking into account combined movements, as was the aim of this study. The preliminary study of this research in which the neuro fuzzy technique was used to classify five distinct simple movements using three channels of signal acquisition, obtained an accuracy of 86% (Favieiro & Balbinot, 2011). Demonstrating the system using only simple movements and a limited number of channels achieved accuracy higher than that found to characterize the combined movements.

The human arm has many degrees of freedom and be able to develop a system that can characterize many different movements and combined is the real challenge of this kind of research, which will really improve the life quality of people with special needs, making them more independent and more likely to real social and economical integration.
