Classification of Hand Gestures Using Time-Frequency Analysis and Different Artificial Intelligence Methods

Hand gesture-based systems are one of the most effective technological advances. Surface electromyography (sEMG) is utilized as a popular input data for gesture classification with elevated accuracy and advanced control capability. In this thesis, which is based on the classification performance of Hilbert-Huang spectrum (HHS) images obtained from Hilbert Huang Transform (HHT) of the sEMG of the gestures, an evaluation of the results of artificial intelligence (AI) methods on hand gesture classification using HHS image is presented.