Desenvolvimento de ferramenta de comparação de técnicas de processamento de sinais para determinar fadiga muscular por meio do sinal emg / Toolkit development for signals processing technics comparison to detect muscular fadigue by EMG signal

AUTOR(ES)
FONTE

IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia

DATA DE PUBLICAÇÃO

09/07/2012

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.

ASSUNTO(S)

emg, fadiga muscular, regressão linear, rms, fft, welch, arma, wavelet. engenharia de software emg muscular fatigue linear regression rms fft welch arma wavelet

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