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Gesture Recognition Based on Multiscale Singular Value Entropy and Deep Belief Network.

Wenguo Li¹, Zhizeng Luo¹, Yan Jin²

¹Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)

|December 30, 2020

Summary

This study introduces a novel gesture recognition method using surface electromyography (sEMG) signals. The approach achieves 93.33% accuracy in recognizing nine gestures, enhancing human-computer interaction for sign language translation.

Keywords:

S-transform deep belief network gesture recognition multiscale singular value decomposition permutation entropy surface electromyography

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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Area of Science:

Human-Computer Interaction
Biomedical Signal Processing
Machine Learning

Background:

Gesture recognition is crucial for human-computer interaction and sign language translation.
Improving the accuracy of gesture recognition using surface electromyography (sEMG) signals is an active research area.

Purpose of the Study:

To propose a new gesture recognition method using four-channel sEMG signals.
To enhance the accuracy and robustness of gesture recognition for sign language translation applications.

Main Methods:

Applying S-transform to four-channel sEMG signals to extract time-frequency characteristics.
Utilizing multiscale singular value decomposition (MSVD) on S-transform outputs for robust joint features.
Calculating singular value permutation entropy (SVPE) as eigenvalues for dimensionality reduction.
Classifying gesture features using a deep belief network (DBN).

Main Results:

The proposed method achieved an average accuracy of 93.33% in recognizing nine distinct gestures.
S-transform effectively enhanced time-frequency details of sEMG signals.
MSVD and SVPE provided robust, low-dimensional features suitable for DBN classification.

Conclusions:

The developed gesture recognition method based on sEMG signals shows high accuracy.
The combination of S-transform, MSVD, and SVPE is effective for feature extraction in gesture recognition.
Multiscale singular value permutation entropy features are particularly well-suited for deep belief network-based pattern classification.