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

Haotian Zhang, Hang Qu, Long Teng (Corresponding Author), Chak Yin Tang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)

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 languageEnglish
Pages (from-to)4749 - 4759
Number of pages11
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume31
DOIs
Publication statusPublished - 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

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