A testosterone pattern-based sEMG signal classification method using Singular Spectrum Analysis

Electromyogram (EMG) signals are crucial to record muscle activity. Several papers have been proposed about EMG signals and mostly machine learning techniques have been used to extract information from EMG signals. In this paper, a molecular-based feature extractor model has been presented. This architecture uses Singular spectrum analysis (SSA) to form sub-bands which are then subjected to statistical feature extraction. The sub-bands are also used to generate textural features using the Local Graph Structure method described in this paper. The feature matrix generated using these methods has been reduced in dimensionality using the Neighborhood Component Analysis (NCA). Finally, an Extreme Learning Machine model for classification has been used for the classification of gestures into their respective classes. The model achieved an accuracy of >97% for 10 classes.