**4.1. Comparison between subjects**

As an example, Figure 15 shows the average accuracy rate for movement M10 for all Subjects. The average score was higher than 70% in all cases. This happened mainly because of the simple movements that compose the M10 movement are easily detectable antagonistic movements. Like the movements M1 and M2 achieved accuracy rates above 75% in most cases.

**Figure 15.** Results of movement M10.

Figure 16 represents the average of all tests performed for each movement. Analyzing the graph it is clear that the more accurate movements were M1, M2, M5 and M10 (combination of M1 and M2), with averages rates of approximately 80%. These movements are quite distinct, which increases the accuracy rate of the system. The worst case occurred with the movement M11, which had an average hit rate below 50%, it combines simple movements used in most of the complex movements performed, impairing the correct recognition. Overall the system achieved an average accuracy of 65%.

**Figure 16.** Overall result of the system for each movement.

Computational Intelligence in Electromyography Analysis – 358 A Perspective on Current Applications and Future Challenges

**4.1. Comparison between subjects** 

**Figure 15.** Results of movement M10.

0

20

40

60

**Accuracy rate (%)**

80

100

Overall the system achieved an average accuracy of 65%.

cases.

**Table 4.** Summary of the system average accuracy rate to the subject 1.

Subject 1 M0 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 Session 2 (%) 100 100 100 100 40 100 80 100 80 100 100 60 Session 3 (%) 40 100 80 80 60 60 100 80 100 100 80 80 Session 4 (%) 60 80 100 80 80 80 100 100 80 100 40 20 Session 5 (%) 60 100 100 100 50 100 20 100 40 60 60 40 Average (%) 65 95 95 90 57 85 75 95 75 90 70 50

As an example, Figure 15 shows the average accuracy rate for movement M10 for all Subjects. The average score was higher than 70% in all cases. This happened mainly because of the simple movements that compose the M10 movement are easily detectable antagonistic movements. Like the movements M1 and M2 achieved accuracy rates above 75% in most

> **Moviment M10 Results: wrist extension followed by wrist flexion**

Figure 16 represents the average of all tests performed for each movement. Analyzing the graph it is clear that the more accurate movements were M1, M2, M5 and M10 (combination of M1 and M2), with averages rates of approximately 80%. These movements are quite distinct, which increases the accuracy rate of the system. The worst case occurred with the movement M11, which had an average hit rate below 50%, it combines simple movements used in most of the complex movements performed, impairing the correct recognition.

Session 2 Session 3 Session 4 Session 5 Session average

Subject 1 Subject 2 Subject 3 Subject 4 Subject 5 Subject 6 Subject 7

#### **4.2. Comparison between the researched results and other studies**

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. As it was possible to see the results of this work, most of the errors were caused by similar movements, or the differentiation of compound movements with their simple 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 where the real challenge and for this reason is an active area of research (Favieiro et al., 2011; Favieiro & Balbinot, 2011).

Comparing the system developed by Chan using fuzzy techniques which were classified four simple movements using only two channels with an accuracy of 91% (Chan, 2000). Also a system was developed by Ajiboye to characterize four classes of movements using four channels, obtaining an accuracy of 86% (Ajiboye, 2005). These systems had similar results to those found in the preliminary study of this research in which the neuro fuzzy technique was used to classify five distinct movements using three channels of signal acquisition, obtaining 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.

Compared with the research of Momen which categorize nine distinct movements, using only the rms characteristic of the window processed and four acquisition channels obtained an average accuracy of 48.9%. This study is very similar to the system developed, in the sense that uses the same characteristic of the signal and making a direct comparison of the average hit rate of the developed system is 30% higher than to 9 moves obtained per Momen (Momen, 2007).

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).

Proposal of a Neuro Fuzzy System for Myoelectric Signal Analysis from Hand-Arm Segment 361

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.

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Gabriela Winkler Favieiro and Alexandre Balbinot *Federal University of Rio Grande do Sul (UFRGS)* 

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**Author details** 

**7. References** 
