Classification of Hand Gestures using sEMG Signals and Hilbert-Huang Transform

Abstract

Artificial intelligence is effectively utilized for hand gesture classification in myoelectric systems. In this study, hand movement classification is performed with ML algorithms using electromyography (EMG) signals of 7 hand gestures. The Hilbert-Huang Transform (HHT) was applied to the preprocessed EMG signals to obtain the Hilbert-Huang spectrum (HHS). Six different Gray Level Co-occurrence Matrix (GLCM)-based features were extracted from HHS images. In order to validate the proposed method, the same features were extracted from the snapshots of EMG signals and intrinsic mode functions (IMF) extracted by empirical mode decomposition (EMD), separately. These features are classified with 29 different Machine learning (ML) approaches in the MATLAB (R) environment. Among these three approaches, the HHS-based novel method yielded the best performance, with an accuracy of 90.87% from the Cubic Support Vector Machine (SVM). The novel HHS and GLCM-based approach may be used in EMG-based biomedical systems as a promising alternative.