**6. Future studies**

It would be very important in future studies performing the tests with a larger number of volunteers, to conduct a robust statistical analysis of results, and allow a more robust assessment of its results, its flaws and strengths. Is ongoing the use of the system with Volunteers with total and partial amputation of the upper limb. This is crucial for research to find out how the system would apply and adapt, it is possible to ascertain the validity of future control system for a prosthetic hand-arm segment by myoelectric signals. Another proposal for further work would find other characteristics that could be extracted from the signal to improve the performance of the fuzzy neural network, so that the system would be able to characterize a wide range of complex movements with a hit ratio above 90%, and a higher capacity to differentiate motion. One way to improve the hit rate of the system is to implement a feedback to the user, to the person performing the test whether they are doing it correctly, avoiding common errors of distraction, or applying excessive force.

### **Author details**

Computational Intelligence in Electromyography Analysis – 360 A Perspective on Current Applications and Future Challenges

**5. Conclusions** 

combined movements.

**6. Future studies** 

Another difference that it is important to note is that the proposed study used only one feature extracted for each channel, unlike other studies that use up to 13 features per channel, whose average accuracy was 87% for the classification of 10 distinct movements (Khushaba, 2010).

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

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

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

It would be very important in future studies performing the tests with a larger number of volunteers, to conduct a robust statistical analysis of results, and allow a more robust assessment of its results, its flaws and strengths. Is ongoing the use of the system with Volunteers with total and partial amputation of the upper limb. This is crucial for research

them more independent and more likely to real social and economical integration.

rate to 11 distinct movements in tests of long duration, about three hours.

Gabriela Winkler Favieiro and Alexandre Balbinot *Federal University of Rio Grande do Sul (UFRGS) Graduate Program in Electrical Engineering (PPGEE), Department of Electrical Engineering, Instrumentation Laboratory, Porto Alegre, Rio Grande do Sul, Brazil* 
