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Related Experiment Video

Updated: May 28, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

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

Published on: March 28, 2025

Myoelectric Gesture Recognition Based on Multiple Mapping and Deep Neural Network.

Shuolei Yin1, Wenjing Huang1, Huicao Xie1

  • 1College of Mechanical and Intelligent Manufacturing, Central South University of Forestry and Technology, Changsha 410004, China.

Biomimetics (Basel, Switzerland)
|May 26, 2026
PubMed
Summary

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MS-CLSTM: Myoelectric Manipulator Gesture Recognition Based on Multi-Scale Feature Fusion CNN-LSTM Network.

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A new "multiple mapping" technique enhances surface electromyography (sEMG) signal feature extraction for improved gesture recognition. This method boosts accuracy in prosthetic control and muscle-computer interaction applications.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Surface electromyography (sEMG) signal analysis is crucial for gesture recognition.
  • Current methods often rely on feature extraction and classification models to enhance accuracy.
  • Challenges remain in developing robust and high-performance representations of sEMG signals.

Purpose of the Study:

  • To introduce a novel feature extraction method,
  • multiple mapping
  • , for sEMG signals.
  • To improve the performance and accuracy of sEMG-based gesture recognition.
  • To explore the application of this method in prosthetic gesture control and muscle-computer interaction.

Main Methods:

  • Developed the
Keywords:
deep neural networkfeature extractionmultiple mappingmyoelectric gesture recognition

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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Related Experiment Videos

Last Updated: May 28, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

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

Published on: March 28, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

  • multiple mapping
  • technique, incorporating sliding average power, base-10 logarithm mapping, linear compression, and sigmoid normalization.
  • Transformed processed sEMG signals into Sem grayscale maps.
  • Utilized the ResNet50 deep neural network for gesture recognition on the generated maps.

    • Main Results:

    • Achieved high average recognition accuracies on public datasets: 95.26%, 90.81%, and 96.72%.
    • Attained 96.8% accuracy in self-collected recognition tasks.
    • Demonstrated significant improvement in feature extraction performance for sEMG-based gesture recognition.

    Conclusions:

    • The
    • multiple mapping
    • method offers a high-performance representation for sEMG signals.
    • This technique substantially enhances the accuracy of gesture recognition.
    • The proposed method shows significant promise for advanced prosthetic gesture control and muscle-computer interaction systems.