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EMG-based hand gesture recognition using multi-scale deep residual network with SE-module.

Ramkumar Sivasakthivel¹, Rajendran Thavasimuthu^2,3, Manikandan Rajagopal⁴

¹Department of Computer Science, School of Sciences, Christ University, Bangalore, Karnataka, India.

Scientific Reports

|June 20, 2026

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This summary is machine-generated.

This study introduces a new deep learning model for hand gesture recognition using EMG signals. The Multi-Scale Deep Residual Network achieves over 99% accuracy, significantly improving human-machine interaction.

Area of Science:

Biomedical Engineering
Machine Learning
Human-Computer Interaction

Background:

Hand Gesture Recognition (HGR) using Electromyography (EMG) signals is crucial for advancing human-machine interaction.
Existing deep learning methods for HGR face challenges in modeling multi-scale temporal variations and channel-wise features in EMG data.
Effectively capturing both temporal dependencies and feature relevance is key to improving HGR performance.

Purpose of the Study:

To propose a novel deep learning-based Multi-Scale Deep Residual Network (DRN) integrated with a Squeeze-and-Excitation (SE) model for enhanced hand gesture recognition using EMG signals.
To address the limitations of current methods in jointly modeling multi-scale temporal dynamics and channel-wise feature importance.
To evaluate the proposed model's effectiveness on the EMG-EPN-612 dataset.

Keywords:

Deep Learning Deep Residual Network EMG HGR Squeeze-and-Excitation

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Main Methods:

EMG signal data collection and preprocessing, including moving average filtering, min-max normalization, and sliding-window segmentation.
Development of a Multi-Scale Deep Residual Network (DRN) incorporating SE-based channel recalibration for capturing multi-scale temporal patterns and feature importance.
Training and testing the model on the EMG-EPN-612 dataset, with 75% for training and 25% for testing.

Main Results:

The proposed Multi-Scale DRN with SE module achieved high performance metrics: 99.24% accuracy, 99.15% precision, 99.17% F1-score, 99.10% specificity, and 99.20% recall.
The model demonstrated superior performance compared to existing methods.
The architecture effectively captured discriminative multi-scale temporal features and adaptively emphasized informative EMG channels.

Conclusions:

The developed DL-based Multi-Scale DRN with SE model offers a significant advancement in EMG-based hand gesture recognition.
The model's ability to jointly learn temporal patterns at different scales and channel importance leads to superior performance.
This approach holds promise for more sophisticated and intuitive human-machine interaction systems.