Wavelet Packet Entropy Based Control of Myoelectric ProsthesisNisheena V. Iqbal, Kamalraj Subramaniam and Shaniba Asmi P.
Department of Electronics and Communication Engineering Karpagam university, Coimbatore India.
Corresponding Author E-mail: firstname.lastname@example.org
Abstract: This paper evaluates the use of wavelet packet entropy to classify upper limb motions using myoelectric signals(MES). Being non-stationary, suitable analysis is essential for myoelectric signals recorded at varying force levels. In this paper, different entropy measures calculated from wavelet packet transform coefficients, termed as wavelet packet entropies (WPE) are compared with power spectral entropy and permutation entropy in terms of their performance in myoelectric prosthetic control. The system was trained using MES corresponding to six upper limb movements at three different force levels. WPE feature was found to exhibit better classification accuracy compared to other entropy features. Among the WPE features log-energy WPE outperformed the other four WPE features; while a combination of log-energy and sure WPE yielded the best classification accuracy when used with a simple linear discriminant analysis(LDA) classifier for medium force level testing.Keywords: Myoelectric; WPE; linear Discriminant; Analysis Back to TOC