Comparison of Hand Gesture Classification from Surface Electromyography Signal between Artificial Neural Network and Principal Component Analysis

The goal of this research is to detect Surface Electromyography (SEMG) signal froma person’s arm using Myo Armband and classify his / her performed finger ges-tures based onthe corresponding signal. Artificial Neural Network (based on the machine learning approach)and Principal Component Analysis (based on the feature extraction approach) with and withoutFast Fourier Transform (FFT) were selected as the methods utilized in this research. Analysisresults show that ANN has achieved 62.14% gesture classifying accuracy, while PCA withoutFFT has achieved 30.43% and PCA without FFT has achieved 48.15% accuracy. The threeclassifiers are tested using SEMG data from a set of six recorded custom gestures. Thecomparison results show that the ANN classifier shows higher classifying accuracy and morerobust rather than the PCA classifier’s classi-fying accuracy. Therefore, ANN classifier is moresuited to be implemented in classifying SEMG signals as hand gestures.