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November 8, 2025Open Access

Electromyography-Based Gesture Recognition: Hierarchical Feature Extraction for Enhanced Spatial-Temporal Dynamics

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Authors

JSJungpil ShinUniversity of AizuAMAbu Saleh Musa MiahUniversity of AizuSKSota KonnaiUniversity of Aizu

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Overview

Analysis shows enhanced spatial-temporal dynamics in gesture recognition, suggesting improvements for assistive technologies.

Key Points

  • Accuracy rates reached 96.41%, 92.40%, and 93.34% across datasets, indicating effective gesture recognition.
  • A deep learning approach was utilized to extract hierarchical features from electromyography signals.
  • The system integrates multiple branches to capture complex spatial-temporal dynamics for improved performance.
  • Findings highlight advancements in assistive technologies and potential for better prosthetic limb control.

Cite This Study

Shin et al. (2025) studied this question.

synapsesocial.com/papers/690e8b6ca5b062d7a4e734e1https://doi.org/10.48550/arxiv.2504.03221
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Deep learning model cascade in electromyography decoding for gesture classification2026
  2. 2Surface EMG-Based Hand Gesture Recognition Using a Hybrid Multistream Deep Learning Architecture2026 · 3 citations
  3. 3Dual Stream Long Short-Term Memory Feature Fusion Classifier for Surface Electromyography Gesture Recognition2024 · 5 citations
  4. 4Hand gestures classification of sEMG signals based on BiLSTM- Metaheuristic Optimization and Hybrid U-Net-MobileNetV2 Encoder Architecture2024 · 3 citations
  5. 5EMG-based hand gesture recognition using multi-scale deep residual network with SE-module2026