LSTM-MSA: A Novel Deep Learning Model With Dual-Stage Attention Mechanisms Forearm EMG-Based Hand Gesture Recognition

• Main Authors: Haotian Zhang (Author), Hang Qu (Author), Long Teng (Author), Chak-Yin Tang (Author)
• Format: Book
• Published: IEEE, 2023-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

This paper introduces the Long Short-Term Memory with Dual-Stage Attention (LSTM-MSA) model, an approach for analyzing electromyography (EMG) signals. EMG signals are crucial in applications like prosthetic control, rehabilitation, and human-computer interaction, but they come with inherent challenges such as non-stationarity and noise. The LSTM-MSA model addresses these challenges by combining LSTM layers with attention mechanisms to effectively capture relevant signal features and accurately predict intended actions. Notable features of this model include dual-stage attention, end-to-end feature extraction and classification integration, and personalized training. Extensive evaluations across diverse datasets consistently demonstrate the LSTM-MSA’s superiority in terms of F1 score, accuracy, recall, and precision. This research provides a model for real-world EMG signal applications, offering improved accuracy, robustness, and adaptability.

Item Description

1558-0210
10.1109/TNSRE.2023.3336865