Article ; Online: LSTM-MSA: A Novel Deep Learning Model With Dual-Stage Attention Mechanisms Forearm EMG-Based Hand Gesture Recognition.
2023 Volume 31, Page(s) 4749–4759
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 ... ...
| 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. |
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| MeSH term(s) | Humans ; Forearm ; Deep Learning ; Electromyography ; Gestures ; Upper Extremity |
| Language | English |
| Publishing date | 2023-12-07 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 1166307-8 |
| ISSN | 1558-0210 ; 1063-6528 ; 1534-4320 |
| ISSN (online) | 1558-0210 |
| ISSN | 1063-6528 ; 1534-4320 |
| DOI | 10.1109/TNSRE.2023.3336865 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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