*3.4.1. Calibration procedure*

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

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

the movements of the virtual model presented to the user.

**3.4. Acquisition and signal pre processing** 

6008 card and store them in a file.

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

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

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

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

• each Subject participates in a single test.

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

Instruments.

movements.

of this stage.

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 of maximum voluntary contraction (MVC).

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 previously conducted with this electromyograph (Favieiro, 2009).

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 by the value found when captured a rest movement.

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 occurring during the process of windowing the signal.

Computational Intelligence in Electromyography Analysis – 352 A Perspective on Current Applications and Future Challenges

Proposal of a Neuro Fuzzy System for Myoelectric Signal Analysis from Hand-Arm Segment 353

Were used mathematical procedures typically used in the myoelectric signal analysis to preprocess the signal and generating one or more characteristics of interest to the

The signal is analyzed in periods of 50 ms, since it provides a comprehensive overview of the signal but, at the same time, specific, since it does not occur in tests muscle relaxation in period shorter than the determined, resulting in an efficient analysis of runtime system and results. To perform the windowing of the signal, the period in which a muscle contraction occurs were developed a routine in Labview which analyses the signal every 50 ms, where each channel is analyzed simultaneously, ensuring if in these data windows occurs a signal peak with value above the threshold. To consider that a movement is taking place is

• is considered that the channel is active if, in the processed time window, there are any peaks above the threshold limit. Considering the threshold a variable that has a value ranging from 30-50% of the respective channel MVC. This percentage is defined

• is necessary that at least three channels have a peak above the respective threshold, i.e., being active. This is done to ensure that any random noise introduced in at least one

• the signal must be considered active, at least 80% of the last 20 windows, i.e., the last one second. The history of activation of the channels is taken into consideration to try to

With these assumptions satisfied, it is considered that a movement is occurring, and in turn, the signal is windowed in all channels simultaneously, considering the same time based for the beginning and end of the muscle contraction. Another assumption considered important is if two sequential movements were spaced in time by up to 3 seconds, they will be considered one single movement and the beginning time of the first movements and the end time of the second are used to define a new single window containing these two movements. Thus, ensuring that complex movements are not considered two or more

The set time of 3 seconds has been based on in the resting time of the Subject, which is 3 seconds. Thus, the windowing of a movement is determined only after a time longer than 3 seconds without the occurrence of a movement, only then is possible to analyze the windows stored in each channel, calculating the rms value from each channel in the

empirically according to preliminary test conducted with a user;

The techniques used are this stage was (for further details consult Favieiro, 2011):

*3.4.2. Preprocessing procedures* 

• removal of DC component (offset adjustment);

necessary to satisfy the following assumptions:

channel interfere with the signal windowing;

ensure that a movement is actually occurring.

distinct movements by the signal windowing.

occurrence time of a muscle contraction.

• determining the rms value of the signal of interest.

classification stage.

• full-wave rectification;

• windowing the signal of interest;

**Figure 11.** Block diagram of the calibration routine.

The movements with MVC used for the calibration of each channel are shown in the table 1.


**Table 1.** Representation of movement defined for each channel calibration.
