Abstract—The purpose of this research is to classify Surface Electromyography (SEMG) signals for normal muscle activity. The aim is to use different extracted features from Continuous Wavelet Transform (CWT) to build and train an Artificial Neural Network (ANN). The extracted features of the SEMG signals used in this research are the mean frequency and the median frequency of the Fourier Power Spectrum along with the RMS values of the signal. These features will be extracted at the selected scales of 8, 16, 32, 64 and 128 of the CWT. The SEMG were collected from normal vastus lateralis and vastus medialis muscles of both legs from 45 male subjects at 25%, 50%, and 75% of their Maximum Voluntary Isometric Contraction (MVIC) force of the quadriceps. Using CWT for extracting features in order to analyse and classify SEMG signals by an ANN has shown to be sound and successful for the basis implementation for developing an intelligent SEMG signal classifier.

Index Terms—Component, electromyography, wavelet, fourier, neural network

The authors are with the School of Engineering at AUT University, Auckland, New Zealand (e-mail: jkilby@ aut.ac.nz; kprasad@laut.ac.nz).

Cite: J. Kilby and K. Prasad, "Continuous Wavelet Analysis and Classification of Surface Electromyography Signals," International Journal of Computer and Electrical Engineering vol. 5, no. 1, pp. 30-35, 2013.