Wavelet signal processing of human muscle electromyography signals : a thesis in partial fulfilment of the requirement for the degree of Masters of Engineering in Mechatronics, Massey University, Albany, New Zealand
Loading...
Date
2010
DOI
Open Access Location
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Massey University
Rights
The Author
Abstract
A novel tool of biosignal processing is proposed to identify human muscle action through
sEMG. The tool is based on the integration of continuous wavelet transforms, the Wavelet time
entropy and the Wavelet frequency entropy to identify muscle actions through sEMG. The
experiments are carried out on triceps, biceps and flexor digitorum superficialis (FDS) muscles.
sEMG signals are measured at different intensities of FDS muscle contractions in order to verify
the consistency of results. By taking the average entropies and basing it on the lowest average
wavelet entropy, it was found in calibrated experiments that the complex Shannon wavelet
family is the best candidate to identify the muscle activities among: derivative of Gaussians
wavelet family, derivative of complex Gaussians wavelet family, complex Morlet family,
Symlets, Coiflets and Daubechies wavelet families. Moreover, the results are consistent with
the time-variant signal. The results presented in this paper have futuristic engineering
implications in biomedical engineering and bio-robotic applications.
The proposed method has the potential of development, improvement and extension to
include other wavelets. Future work includes compromising two wavelets that have different
properties on both time and frequency domains, such as the complex Shannon wavelet (with
very good frequency resolution but a slow decay in the time domain) and the Meyer wavelet
(with good frequency resolution but a faster decay than the complex Shannon wavelet in the
time domain), in order to produce optimal results of Wavelet time entropy and Wavelet
frequency entropy.
Description
Keywords
Biosignal processing, Complex Shannon wavelet family, Human muscle contractions, Continuous wavelet transforms, sEMG, Surface electromyography