Using non-iterative methods and random weight networks to classify upper-limb movements through sEMG signals.

Abstract

This paper presents the use of two non-iterative methods to perform the classification of 17 different upper-limb movements through sEMG signal processing. The two methods were compared with a SVM classifier using three different databases involving amputee subjects. The non-iterative methods presented equivalent or superior classification accuracy than SVM method. Thereafter a stage of PCA pre-processing method was used in order to promote a better class separation prior the non-iterative classifiers. The best accuracy result without PCA was achieved by the Regularized Extreme Learning Machines algorithm (88,4% for non-amputee subjects and 79,4% for the amputee). The PCA method used boosted the accuracy of the two non-iterative methods which the mean accuracy was 94% for the non-amputee subjects and 85% for the amputee subjects.