Comparison between kNN and SVM for EMG Signal Classification

Abstract

This paper shows an approach for EMG signal processing and classification as a tool to classify neuromuscular disorder. EMG signal classification is an emerging field of science and engineering providing efficient way for diagnosing neuromuscular disorder. Several techniques have been suggested for classification of EMG signals.This paper shows an approach for EMG signal processing and classification based on discrete wavelet transform as a tool to extract important information such as approximate and detail coefficients. Present work shows the comparison of kNN (k-Nearest Neighbours) and Support vector machine