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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Al-Khazzar, Ahmed M.
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Computer Science & IT Engineering

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Using deep neural networks in classifying electromyography signals for hand gestures Al-Khazzar, Ahmed M.; Altaweel, Zainab; Hussain, Jabbar S.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp217-227

Abstract

Electromyography (EMG) signals are used for various applications, especially in smart prostheses. Recognizing various gestures (hand movements) in EMG systems introduces challenges. These challenges include the noise effect on EMG signals and the difficulty in identifying the exact movement from the collected EMG data amongst others. In this paper, three neural network models are trained using an open EMG dataset to classify and recognize seven different gestures based on the collected EMG data. The three implemented models are: a four-layer deep neural network (DNN), an eight-layer DNN, and a five-layer convolutional neural network (CNN). In addition, five optimizers are tested for each model, namely Adam, Adamax, Nadam, Adagrad, and AdaDelta. It has been found that four layers achieve respectable recognition accuracy of 95% in the proposed model. 
Co-Authors Altaweel, Zainab Hussain, Jabbar S.