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MVMD-TCCA: A method for gesture classification based on surface electromyographic signals.

Wenjie Chen¹, Shenke Zhang¹, Xiantao Sun¹

¹School of Electrical Engineering and Automation, Anhui University, Hefei, China.

Journal of Electromyography and Kinesiology : Official Journal of the International Society of Electrophysiological Kinesiology

|April 2, 2025

Summary

This study introduces a new method for accurate gesture recognition using surface electromyographic (sEMG) signals. The proposed approach significantly improves classification accuracy for motor intention recognition, aiding individuals with motor impairments.

Keywords:

Attention mechanisms Gesture recognition Multivariate variational modal decomposition Surface electromyographic signals

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Area of Science:

Biomedical Engineering
Signal Processing
Machine Learning

Background:

Gesture recognition is crucial for nonverbal communication and assisting individuals with motor impairments.
Surface electromyographic (sEMG) signals are key for detecting motor intentions and accurate gesture classification.

Purpose of the Study:

To enhance the accuracy of gesture classification for motor intention recognition using sEMG signals.
To introduce a novel method combining multivariate variational mode decomposition and a two-channel convolutional neural network with attention.

Main Methods:

Proposed the multivariate variational mode decomposition and two-channel convolutional neural network with attention (MVMD-TCCA) method.
Utilized MVMD to decompose and fuse sEMG signals for improved feature representation.
Integrated CBAM and CrissCross attention mechanisms into the CNN for enhanced local and spatial feature learning.

Main Results:

Achieved 85.09% average classification accuracy on the NinaPro DB2 dataset, a 13.46% improvement over the original signal.
Attained 97.90% average classification accuracy on a dataset from 15 subjects, a 1.70% improvement.
Demonstrated significant enhancement in gesture classification performance.

Conclusions:

The MVMD-TCCA method effectively improves sEMG-based gesture classification accuracy.
Accurate gesture recognition holds significant potential for assisting patients with motor impairments, such as those with cerebral infarction.