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Related Experiment Video

Updated: Aug 24, 2025

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

Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model.

Dianchun Bai^1,2, Tie Liu¹, Xinghua Han¹

¹School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China.

Cyborg and Bionic Systems (Washington, D.C.)

|October 26, 2022

Summary

This study introduces a deep learning model for accurate gesture recognition using surface electromyography (sEMG) signals. The optimized model achieves high accuracy on embedded systems, enabling precise control of prosthetic devices.

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Area of Science:

Biomedical Engineering
Artificial Intelligence
Human-Computer Interaction

Background:

Deep learning for gesture recognition using surface electromyography (sEMG) is crucial for advanced human-computer interaction.
Existing models often require significant storage space, limiting their application on embedded systems.
Multistate muscle action recognition demands high accuracy and efficient models.

Purpose of the Study:

To develop and optimize a feature model for multichannel sEMG signals for accurate multistate muscle action recognition.
To create a compact deep learning model suitable for embedded chip deployment with limited storage.
To enhance the accuracy and applicability of gesture recognition in human-computer interaction.

Main Methods:

Constructed a feature model using multidimensional sequential sEMG images.
Combined Convolutional Neural Network (CNN) and Long-Term Memory (LTM) network for sEMG signal recognition.
Utilized Fast Fourier Transform (FFT) and Root Mean Square (RMS) for feature data processing.

Main Results:

Achieved a high recognition rate for sEMG signals processed with FFT and RMS.
Demonstrated a 91.40% recognition accuracy for complex gestures.
The optimized model has a compact size of 1MB, suitable for embedded applications.

Conclusions:

The proposed feature model and optimization method enable accurate multistate sEMG signal recognition.
The compact and high-precision deep learning model can effectively control artificial hands.
This approach advances the development of efficient and accurate gesture recognition systems for embedded human-computer interaction.