Abstract:
In this paper, a novel feature extraction method
based on the utilization of wavelet packet transform (WPT) and
the concept of fuzzy entropy is presented. The method acts in
steps, were in the first step the WPT is employed to generate a
wavelet decomposition tree from which many features are
extracted. In the second step, a new algorithm to compute the
fuzzy entropy is developed and adopted as a measure of
information content to judge on features suitability in
classification, by setting a threshold and removing the features
that fall under a certain threshold. In the third step, principle
component analysis (PCA) is employed to reduce the
dimensionality of the generated feature set. As an application,
the new algorithm is employed in multifunction myoelectric
control problem to prove its efficiency. Accurate results (99%
accuracy) are acquired from using only a small subset of the
original feature set generated by the wavelet tree. The obtained
results indicate that the generated features are of maximum
relevance and with minimum degree of redundancy.
