Novel Hand Gesture Classification based on Empirical Fourier Decomposition of sEMG Signals*

Major challenge in building models for stroke rehabilitation stems from the non stationarity of the EMG signals. In this work we present a methodology for improved classification of hand gestures using Empirical Fourier Decomposition (EFD). First we apply the EFD technique on a set of publicly available dataset and later we reduce the dimensionality to collect most significant components. Finally we extract features from these components and perform hand gesture classifications using different machine learning (ML) models.Clinical Relevance: Compared to the state-of-art Empirical Wavelet Transform (EWT), the EFD technique reduced the total significant components considerably. To capture 90% of information from original data, the EFD approach needed 5.96% and 23.21% less number of components compared to EWT approach for original and dimensionally reduced data sets respectively. The classification models using EFD components gave an average 3.4% accuracy improvement compared to that of EWT components.