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Updated: Jul 23, 2025

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

IRDC-Net: An Inception Network with a Residual Module and Dilated Convolution for Sign Language Recognition Based on

Xiangrui Wang¹, Lu Tang¹, Qibin Zheng¹

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Sensors (Basel, Switzerland)

|July 14, 2023

Summary

This summary is machine-generated.

This study introduces a novel Inception architecture with residual module and dilated convolution (IRDC-net) for sign language recognition (SLR) using surface electromyography (sEMG) signals. The IRDC-net significantly improves classification accuracy for deaf communication aids.

Keywords:

dilated convolution inception network residual module sign language recognition surface electromyogram

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Area of Science:

Biomedical Engineering
Artificial Intelligence
Human-Computer Interaction

Background:

Deaf and hearing-impaired individuals face communication challenges.
Surface electromyography (sEMG) based sign language recognition (SLR) offers a promising solution for social integration.
Traditional convolutional neural network (CNN) structures have limitations in capturing complex sEMG signal features.

Purpose of the Study:

To propose a novel IRDC-net architecture for enhanced SLR.
To improve the accuracy and efficiency of recognizing Chinese sign language signs.
To validate the proposed method on a public dataset and compare it with existing CNN models.

Main Methods:

Transformation of time-domain sEMG signals to the time-frequency domain using discrete Fourier transformation.
Development and application of a novel Inception architecture with residual module and dilated convolution (IRDC-net) for SLR.
Comparative analysis of IRDC-net against VGG-net and ResNet-18 using the Ninapro DB1 dataset.

Main Results:

The IRDC-net achieved a classification accuracy of 91.70% on the custom Chinese sign language dataset after time-frequency transformation.
On the public Ninapro DB1 dataset, the IRDC-net reached a classification accuracy of 89.82% for time-frequency data.
The proposed IRDC-net outperformed VGG-net and ResNet-18 in SLR tasks.

Conclusions:

The IRDC-net effectively captures intricate features from sEMG signals for improved SLR.
Time-frequency domain transformation enhances the performance of sEMG-based SLR systems.
This research contributes to advancing SLR technology and aiding deaf and hearing-impaired individuals.