Title :
EMG Pattern Recognition System Based on Neural Networks
Author :
Gonzalez-Ibarra, Juan Carlos ; Soubervielle-Montalvo, Carlos ; Vital-Ochoa, Omar ; Perez-Gonzalez, Hector Gerardo
Author_Institution :
Centro de Investig. y Estudios de Posgrado (CIEP), Univ. Autonoma de San Luis Potosi (UASLP), San Luis Potosí, Mexico
fDate :
Oct. 27 2012-Nov. 4 2012
Abstract :
In this document we present a methodology for movement pattern recognition from arm-forearm myoelectric signals, starting off from the design and implementation of an electromyography (EMG) instrumentation system, considering the Surface EMG for the Non Invasive Assessment of Muscles (SENIAM) rules. Signal processing and characterization techniques were applied using the pass-band Butter worth digital filter and fast Fourier transform (FFT). Artificial neural networks (ANN) such as back propagation and radial basis function (RBF) were used for the pattern recognition or classification of the EMG signals. The best results were obtained using the RBF ANN, achieving an average accuracy of 98%.
Keywords :
Butterworth filters; backpropagation; band-pass filters; digital filters; electromyography; fast Fourier transforms; handicapped aids; medical signal processing; pattern classification; radial basis function networks; signal classification; EMG pattern recognition system; EMG signals; FFT; RBF ANN; SENIAM rules; arm-forearm myoelectric signals; artificial neural networks; back propagation; electromyography instrumentation system; fast Fourier transform; man-machine interface; motor disabilities; movement pattern recognition; pass-band Butter worth digital filter; pattern classification; radial basis function; signal characterization technique; signal processing technique; surface EMG non invasive assessment-of-muscles rules; Accuracy; Artificial neural networks; Electrodes; Electromyography; Muscles; Neurons; Pattern recognition; Artificial Neural Networks; EMG; FFT; Pattern Recognition;
Conference_Titel :
Artificial Intelligence (MICAI), 2012 11th Mexican International Conference on
Conference_Location :
San Luis Potosi
Print_ISBN :
978-1-4673-4731-0
DOI :
10.1109/MICAI.2012.23
