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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals from Two sEMG

Hope O Shaw1, Kirstie M Devin1, Jinghua Tang1

  • 1School of Engineering, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK.

Sensors (Basel, Switzerland)
|April 27, 2024
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Summary

This study demonstrates high accuracy in myoelectric hand control using only two surface electromyography (sEMG) electrodes. Machine learning algorithms, particularly SVM, achieved performance comparable to multi-electrode systems, enhancing prosthetic functionality.

Keywords:
classificationhand actionsmachine learningmyoelectric prostheticssEMGupper limb

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Human-Computer Interaction

Background:

  • Classification-based myoelectric control enables advanced prosthetic hand functionality.
  • High accuracies often rely on multiple sEMG electrodes or additional sensors.
  • Two-electrode sEMG systems are common but lack detailed performance studies.

Purpose of the Study:

  • To investigate the classification performance of signal processing and machine learning algorithms using two sEMG electrodes.
  • To compare the effectiveness of LDA, KNN, and SVM for myoelectric hand control.
  • To identify optimal feature selection strategies for two-electrode systems.

Main Methods:

  • Acquired sEMG signals from nine participants performing six hand actions using a two-electrode Delsys Trigno system.
  • Applied signal processing techniques and machine learning algorithms (LDA, KNN, SVM).
  • Analyzed classification accuracy, action-specific accuracy, and F1-score, exploring feature-accuracy relationships.

Main Results:

  • Achieved overall classification accuracy of 93 ± 2% and action-specific accuracy of 97 ± 2%.
  • SVM algorithm outperformed LDA and KNN, yielding an F1-score of 87 ± 7%.
  • Classification accuracy showed a logarithmic relationship with the number of features, plateauing at five features.

Conclusions:

  • Two-electrode sEMG systems can achieve high classification accuracies for myoelectric control.
  • SVM offers superior performance for this application compared to LDA and KNN.
  • Findings support improved signal processing and machine learning strategies for widely used two-electrode myoelectric hand systems.