**4. Results and discussion**

This topic will discuss the test performed during system development, and the results obtained. It is important to note that the pre-processing routine and calibration have already been validated in previous studies (Favieiro, 2009; Favieiro & Balbinot, 2011).

Subjects participating in this research present an age range of 20±5 years old, of both sexes. Altogether were conducted trials with seven Subjects. The abbreviations of the characterized movements are presented in Table 3.

It is worth noting that the parameters of ANFIS training were the same for all Subjects. Figure 14 represents the result of section 2 for Subject 1 where is possible notice that for the movement M11 that is the hand contraction with the forearm flexion, 40% of the error was due to the fact that the network believed was dealing with movement M0 (hand contraction) which represents partially the movement performed. Another movement in which occurred the incorrect recognition was the M4 (forearm rotation) with M3 (forearm flexion), causing 60% of error, which may occur since these movements uses muscle in common, such as the biceps, and were used only surface electrodes.

As an example, Table 4 represents the average accuracy rate of the system for each movement per session, and the overall average of each movement per session for the Subject 1. The movements with lower hit rate are: hand contraction (M0), forearm rotation (M4) and hand contraction and forearm rotation (M11), with 65%, 57% and 50% hit rate, respectively. It happened by the similarity of M4 to M3 (forearm flexion) and by the similarity between M0 to M11.


**Table 3.** Abbreviations representing the performed movements.

Computational Intelligence in Electromyography Analysis – 356 A Perspective on Current Applications and Future Challenges

**Figure 13.** Training routine of the neuro-fuzzy network.

the training data.

**4. Results and discussion** 

movements are presented in Table 3.

biceps, and were used only surface electrodes.

network cannot reach its best performance (under fitting);

mean square error falls below a predetermined value α.

• number of training epochs: the value 10 was selected , which defines the number of training cycles, i.e., the maximum number of times the training set is presented to the network. An excessive number of cycles can lead to loss of power to the network generalization (over fitting). On the other hand, with a small number of cycles, the

• target error: the value 0 was selected, which consists in terminate the training after the

As output of the training step, is generated a fuzzy network with adapted membership functions to a particular Subject, causing the limits of each functions to be left according to

This topic will discuss the test performed during system development, and the results obtained. It is important to note that the pre-processing routine and calibration have already

Subjects participating in this research present an age range of 20±5 years old, of both sexes. Altogether were conducted trials with seven Subjects. The abbreviations of the characterized

It is worth noting that the parameters of ANFIS training were the same for all Subjects. Figure 14 represents the result of section 2 for Subject 1 where is possible notice that for the movement M11 that is the hand contraction with the forearm flexion, 40% of the error was due to the fact that the network believed was dealing with movement M0 (hand contraction) which represents partially the movement performed. Another movement in which occurred the incorrect recognition was the M4 (forearm rotation) with M3 (forearm flexion), causing 60% of error, which may occur since these movements uses muscle in common, such as the

As an example, Table 4 represents the average accuracy rate of the system for each movement per session, and the overall average of each movement per session for the Subject 1. The movements with lower hit rate are: hand contraction (M0), forearm rotation (M4) and

been validated in previous studies (Favieiro, 2009; Favieiro & Balbinot, 2011).

**Figure 14.** System output for Subject 1 – section 2 (5 repetitions).

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


Proposal of a Neuro Fuzzy System for Myoelectric Signal Analysis from Hand-Arm Segment 359

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

0

50

**Accuracy rate (%)**

100

(Favieiro et al., 2011; Favieiro & Balbinot, 2011).

that found to characterize the combined movements.

(Momen, 2007).

**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

M0 M1 M2 M3 M4 M5 M6

M7 M8 M9 M10 M11 All

Overall average

**Overall average for each movement**

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

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

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