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

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.

EFE-LSTM: A Feature Extension, Fusion and Extraction Approach Using Long Short-Term Memory for Navigation Aids State Recognition

“…An et al [15], based on the principles of calculus and in conjunction with LSTM networks and a novel loss function, significantly enhanced the accuracy and efficiency of state recognition under time-varying operating conditions. Alotaibi et al [16] developed a model that integrates LSTM with attention mechanisms to enhance the accuracy of electromyographic signal recognition. By effectively incorporating the advantages of time series analysis and focused attention, the experiment yielded a high average accuracy of 91.5%.…”