An ensemble of long short-term memory networks with an attention mechanism for upper limb electromyography signal classification

Alotaibi, Naif D., Jahanshahi, Hadi, Yao, Qijia, Mou, Jun and Bekiros, Stelios (2023) An ensemble of long short-term memory networks with an attention mechanism for upper limb electromyography signal classification. Mathematics, 11 (18). ISSN 2227-7390

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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.

Item Type:	Article
Official URL:	https://www.mdpi.com/journal/mathematics
Additional Information:	© 2023 The Author(s)
Divisions:	LSE Health
Subjects:	Q Science > QA Mathematics
Date Deposited:	21 May 2024 12:09
Last Modified:	17 Jul 2024 17:27
URI:	http://eprints.lse.ac.uk/id/eprint/123550

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