Beta Artificial Bee Colony Algorithm for EMG Feature Selection

Abstract

The electromyography (EMG) signals play a very important role in the muscular activity of the human body. Muscular activities can be measured and analyzed through various movements such as writing, walking, running, hand movements. This paper is focused on hand movements of the amputee people (people with prosthetic hands). Classification of prosthetic hand movements is a research problem that can be solved by classifying the EMG signal generated during movements. The EMG signal has many redundant and irrelevant features that need to be removed. Feature selection (FS) is a technique that is used to select the important features. Therefore, this paper proposes beta artificial bee colony (BetaABC) and binary BetaABC (BBABC) to select prominent features in EMG pattern recognition to increase classification performance and to reduce classifier complexity. BABC is the enhanced version of the ABC inspired from Gaussian ABC (GABC). Initially, discrete wavelet transform (DWT) was applied to decompose and extract the features from EMG signal. After that, BBABC is applied to select the optimal features from the original feature set. The NinaPro dataset (DB3) is used for the experiment and is divided into two sections: (i) Comparative results of proposed BetaABC with ABC and GABC on 10 benchmark functions; (ii) comparative results of proposed BBABC-based feature selection with binary ABC (BABC) and binary GABC (BGABC) on 11 amputee subjects. The obtained results prove that the proposed method is superior to ABC and GABC.