Clustering analysis and pattern discrimination of EMG linear envelopes.

Abstract

A technique has been developed for performing pattern analysis of EMG activities generated during locomotion. In this development it was found that the shapes of the EMG linear envelopes (LE) are mainly determined by their phase spectra; their magnitude spectra are much less important. Autoregressive (AR) parametric models and discrete Fourier transform (DFT) approaches were tested and compared. The latter was proved to be a better way to describe the EMG LE's. Feature extraction and clustering were performed by doing DFT of EMG LE's, extracting part of the phase and magnitude spectra (in less important degree) as features, and using the percent powers to weigh the corresponding harmonics. The approach was applied to the clustering analysis of EMG LE's of normal and anterior cruciate ligament (ACL) injured subjects during walking.