An Ensemble of Long Short-Term Memory Networks with an Attention Mechanism for Upper Limb Electromyography Signal Classification

Naif D. Alotaibi, Hadi Jahanshahi (), Qijia Yao, Jun Mou and Stelios Bekiros
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Naif D. Alotaibi: Communication Systems and Networks Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Hadi Jahanshahi: Institute of Electrical and Electronics Engineers, Toronto, ON M5V 3T9, Canada
Qijia Yao: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Jun Mou: School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China

Mathematics, 2023, vol. 11, issue 18, 1-21

Abstract: Advancing cutting-edge techniques to accurately classify electromyography (EMG) signals are of paramount importance given their extensive implications and uses. While recent studies in the literature present promising findings, a significant potential still exists for substantial enhancement. Motivated by this need, our current paper introduces a novel ensemble neural network approach for time series classification, specifically focusing on the classification of upper limb EMG signals. Our proposed technique integrates long short-term memory networks (LSTM) and attention mechanisms, leveraging their capabilities to achieve accurate classification. We provide a thorough explanation of the architecture and methodology, considering the unique characteristics and challenges posed by EMG signals. Furthermore, we outline the preprocessing steps employed to transform raw EMG signals into a suitable format for classification. To evaluate the effectiveness of our proposed technique, we compare its performance with a baseline LSTM classifier. The obtained numerical results demonstrate the superiority of our method. Remarkably, the method we propose attains an average accuracy of 91.5%, with all motion classifications surpassing the 90% threshold.

Keywords: EMG signals; time series classification; neural network; LSTM; attention mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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