Abstract
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.
Original language | English |
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Pages (from-to) | 4749 - 4759 |
Number of pages | 11 |
Journal | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
Volume | 31 |
DOIs | |
Publication status | Published - 28 Nov 2023 |
Keywords
- Electromyography
- signal processing
- deep learning
- attention mechanism
- LSTM
- hand gesture recognition
ASJC Scopus subject areas
- General Computer Science
- Signal Processing
- General Engineering