Analysis of Extracted Forearm sEMG Signal Using LDA, QDA, K-NN Classification Algorithms

A surface electromyographic (sEMG) signal includes important information on muscular activity and was recently widely used as an input signal in a myoelectric control system. In this manuscript, eight hand motions were classified using different extracted features from sEMG signals. The results of the experiment show that the combination of sample entropy (SampEnt), root mean square (RMS), myopulse percentage rate (MYOP), and difference absolute standard deviation value (DASDV) achieved the highest classification rate of 98.56% using the linear discriminant analysis (LDA) classifier. Moreover, this study investigated the best value of K that should be used as an input parameter in the K-nearest neighbor (K-NN) algorithm. The result demonstrates that k = 5 is the optimal choice in most cases.