EMG pattern recognition system based on neural networks Conference Paper

•  
• Overview
•  
• Research
•  
• Identity
•  
• Additional Document Info
•  
• View All
•  

Overview

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%25. © 2012 IEEE.

authors

• Gonzalez-Ibarra J.C.
• Pérez González Héctor Gerardo
• Soubervielle Montalvo Carlos
• Vital Ochoa Omar

publication date

• 2012-01-01

published in

• Proceedings of Special Session - Revised Papers, 11th Mexican International Conference on Artificial Intelligence 2012: Advances in Artificial Intelligence and Applications, MICAI 2012  Proceedings

Research

keywords

• Artificial Neural Networks; EMG; FFT; Pattern Recognition Characterization techniques; EMG; EMG signal; Instrumentation systems; Myoelectric signals; Non-invasive assessments; Pass bands; Radial basis functions; Surface EMG; Digital filters; Electromyography; Neural networks; Pattern recognition; Pattern recognition systems; Radial basis function networks; Signal processing; Fast Fourier transforms

Identity

Digital Object Identifier (DOI)

• 10.1109/MICAI.2012.23

Additional Document Info

start page

• 71

end page

• 74