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

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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.
Biosignal processing, Complex Shannon wavelet family, Human muscle contractions, Continuous wavelet transforms, sEMG, Surface electromyography