Comparison of signal processing methods used for classification of EMG signals; [EMG işaretlerini siniflamada kullanilan işaret işleme tekniklerinin karşilaştirilmasi]

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2005

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Abstract

The Electromyographic (EMG) signals observed at the surface of the skin is the sum of many small action potentials generated in the muscle fibers. There is only a pattern for each EMG signals, which are generated by biceps and triceps muscles. This pattern consists of information of direction and speed of movement. There are different types of signal processing to find feature values for true classification in this pattern. In this study, the Feature values belong to 4 different arm movements are obtained by using Autoregressive parameters (AR), Fast Fourier Transform (FFT), and Wavelet Transform. Then these feature values are compared each other by using same classifier. The Back-Propagation Algorithm, which has 3 layer perception, was preferred as a classifier. © 2005 IEEE.

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Keywords

Bioelectric potentials , Fast Fourier transforms , Muscle , Pattern recognition , Signal filtering and prediction , Skin , Wavelet transforms , Action potentials , Autoregressive parameters (AR) , Back Propagation Algorithms , Electromyography

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http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/19584

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