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Recently, wavelet analysis has proved to be one of the most powerful signal processing tools for the analysis of surface electromyography (sEMG) signals. It has been widely used in sEMG pattern classification for both clinical and engineering applications. This study investigated the usefulness of extracting sEMG features from multiple-level wavelet decomposition and reconstruction. A suitable wavelet based function was used to yield useful resolution components from the sEMG signal. The optimal sEMG resolution component was selected and then its reconstruction carried out. Throughout this process, noise and unwanted sEMG components were removed. Effective sEMG components were extracted with twenty-five state-of-the-art features in both the time domain and the frequency domain. Two criteria were deployed in the evaluation, scatter graphs and a class separation index. The experimental results show that most sEMG features extracted from the reconstructed sEMG signal of the first and second-level wavelet detail coefficients yield improved class separability in feature space. Some features extracted from the sub-signals are recommended such as the myopulse percentage rate, zero crossing, Willison amplitude and the mean absolute value. The proposed method will ensure that the classification accuracy will be as high as possible while the computational time will be as low as possible. Ill. 3, bibl. 24, tabl. 2 (in English; abstracts in English and Lithuanian).DOI: http://dx.doi.org/10.5755/j01.eee.122.6.1816
Phinyomark et al. (Thu,) studied this question.