**3.3. Experimental procedures**

All the experiments were carried out with consent of the Subjects, according to the ethical precepts and respecting the bio signal acquisition techniques (in this case related to the myoelectric signal acquisition), like for instance the treatment of the skin, electrode positioning among other aspects.

For the data acquisition the NI USB 6008 board was used. Eight pairs of electrodes located in the main muscle groups of the Subject were been used, which are the main part of the movements that were chosen to characterize: Biceps (C0), palmaris longus (C1), flexor carpi ulnaris (C2), flexor carpi radialis (C3), pronator teres (C4), extensor digitorum (C5), brachioradialis (C6) and extensor carpi ulnaris (C7), as shown in Figure 8.

Proposal of a Neuro Fuzzy System for Myoelectric Signal Analysis from Hand-Arm Segment 349

**Figure 7.** Diagram representing the videos of complex movements developed as a virtual model.

To start the acquisition, after correct positioning of the electrodes, the Subject is instructed to replicate the animations of the virtual model, which appear on the LCD screen, using a moderate strength. In order to standardize the testing of signal acquisition was adapted to

the methodology proposed by Li (Li, 2010), considering the following aspects:

• is generated a random sequence of animations for each session of the test; • each session is composed of 5 repetitions of each of the 12 selected movements;

• between movements, the Subject should rest for 3 seconds;

**Figure 8.** Picture showing the electrodes positions.

• each test consists of 5 sessions;

**Figure 6.** Diagram representing the videos of simple movements developed as a virtual model.

**Figure 7.** Diagram representing the videos of complex movements developed as a virtual model.

**Figure 8.** Picture showing the electrodes positions.

To start the acquisition, after correct positioning of the electrodes, the Subject is instructed to replicate the animations of the virtual model, which appear on the LCD screen, using a moderate strength. In order to standardize the testing of signal acquisition was adapted to the methodology proposed by Li (Li, 2010), considering the following aspects:

• each test consists of 5 sessions;

Computational Intelligence in Electromyography Analysis – 348 A Perspective on Current Applications and Future Challenges

**Figure 6.** Diagram representing the videos of simple movements developed as a virtual model.


Computational Intelligence in Electromyography Analysis – 350 A Perspective on Current Applications and Future Challenges

• each Subject participates in a single test.

In the figure 9 is shown a picture of one of the sessions.

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According to the flowchart shown in Figure 10, the data acquisition and processing are used simultaneously, since it is possible to perform parallel routines in Labview (note that the

The calibration of the system aims to achieve specific characteristics of the voluntary muscle signal, as each person may have a different muscle activity. Thus, the calibration allows that the system is generic and therefore adapts itself to different users. The calibration procedure of the system involves capturing the muscle signal in a time of relaxation and in a moment

Figure 11 shows a brief block diagram of the calibration procedure. This step involves eight Boolean variables (SNR (x), x = 1.2 .. 8) indicating that all channels of myoelectric signal acquisition were correctly calibrated. If a pair of electrodes is not properly positioned, the distress signal has low quality, and once again must be repositioned until the signal / noise ratio is at least greater than 2 - value established based on the signal acquisition trials

For the calibration of each channel, initially an acquisition of the signal is performed with the muscle in rest position. Then the signal is processed to calculate the average peak values. Later on a MVC movement is performed and captured, and after this, again the average peak value is calculated. With this information is possible to evaluate the signal to noise ratio (SNR) that is given by dividing the value processed during the movement with MVC

A percentage ranging from 30-50% of the average peak values of the acquired signal with the maximum voluntary contraction (MVC) is then used to determine, during processing of the signal, the threshold value which indicates whether or not a muscle contraction

**Figure 10.** Flowchart of the online acquisition routine.

of maximum voluntary contraction (MVC).

*3.4.1. Calibration procedure* 

real parallelism is only supported from the Labview 2010 package).

previously conducted with this electromyograph (Favieiro, 2009).

by the value found when captured a rest movement.

occurring during the process of windowing the signal.

**Figure 9.** Picture of a session.

A Labview routine was developed to interface with Matlab to generate the sequence of movements (See figures 6 and 7) randomly. The output is a vector with a random order of the movements of the virtual model presented to the user.

#### **3.4. Acquisition and signal pre processing**

The programming language chosen for the development of the proposed system software is Labview (Laboratory Virtual Instrument Engineering Workbench) from National Instruments.

The acquisition and generation of the myoelectric signals database were obtained through a routine created in Labview software to read the input data acquired through the NI USB 6008 card and store them in a file.

To choose the sample rate was considered that the myoelectric signal of interest in this work is in the range 20-500 Hz, and most of the energy of this signal is in the frequency range 50- 150 Hz based on this information, the sampling frequency used was 1 kHz which is suitable for the proposed system. For this specification, 1 ms was sufficient to identify the user movements.

The online acquisition is performed in a way that the signal is transferred to the computer in time windows of 50 ms, thought the acquisition board, and the signal is stored in a FIFO (First In First Out) queue, in which the stored time windows are being processed according to the acquisition order, ensuring no data loss. Figure 10 shows the corresponding flowchart of this stage.

**Figure 10.** Flowchart of the online acquisition routine.

According to the flowchart shown in Figure 10, the data acquisition and processing are used simultaneously, since it is possible to perform parallel routines in Labview (note that the real parallelism is only supported from the Labview 2010 package).
