Desenvolvimento de ferramenta de comparação de técnicas de processamento de sinais para determinar fadiga muscular por meio do sinal emg
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Data
2012-07-09
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Universidade Federal de Goiás
Resumo
This study aimed to development of a computational tool for electromyographic signal
(EMG) analysis by signal processing techniques to determine muscular fatigue. With
Ethics Committee of Federal University of Goiás approve were recorded from the
dominant biceps brachii of 10 volunteers, that did not ever had muscular disease.
The protocol consisted on get the maximal voluntary isometric contraction (MVIC)
from the volunteer seated, floor contact with the feet, and forearm in 90 degree,
doing three maximal voluntary contraction against a rigid and fixed surface, by five
seconds, with a five resting minutes between each acquisition. The MVIC values
were obtained by arithmetical mean from the three greater values of each
contraction. In statistical analysis the volunteer sustained a load of 60% MVIC for 30
seconds, or while they supported. For dynamical analysis was used a
electrogoniometer tied in forearm to measure the angle and a 60% MVIC load for 30
seconds measured, achieved angle of 70° until 130°, and return to 70°. Each flexion
was did in 1,5 seconds, or while volunteer support. To analyze the signal in time
domain were used Root main square (RMS) values and Continuous wavelet
transform (CWT). To analyze in frequency domain were adopted the values of mean
and median from Fast Fourier Transform (FFT), Welch spectral estimator, auto
regressive moving average (ARMA) filter, and analytic wavelet transform (AWT).
Linear regressions were obtained using a window of 250 ms for all techniques.
Slopes with positive values, in time domain, and slopes with negative values, in
frequency domain, indicate muscular fatigue. Using high scales of wavelet transform
shows great results while compared with default techniques, like RMS and FFT.
ANOVA one way were adopted as statistical method of analysis, with P < 0,05. Only
in dynamic contraction, on frequency domain, had P value < 0,05. Tukey test were
applied to identify which techniques had variance great than 5%. Is suggested as
future works development of a wireless system to acquire EMG signals, improvement
in the software to motor unit action potentials (MUAP) detection, prosthesis control,
and synchronization with others systems of data collection.
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CAMPOS, Ramon de Freitas Elias. Toolkit development for signals processing technics comparison to detect muscular fadigue by EMG signal. 2012. 122 f. Dissertação (Mestrado em Engenharia) - Universidade Federal de Goiás, Goiânia, 2012.