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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Gesture Recognition by Ensemble Extreme Learning Machine Based on Surface Electromyography Signals.

Fulai Peng1, Cai Chen1, Danyang Lv1

  • 1Medical Rehabilitation Research Center, Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, China.

Frontiers in Human Neuroscience
|July 5, 2022
PubMed
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This study enhances gesture recognition using surface electromyography (sEMG) signals by combining feature selection with an ensemble extreme learning machine (EELM). The novel EELM method achieved superior accuracy compared to traditional algorithms for hand movement classification.

Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Surface electromyography (sEMG) signals are crucial for gesture recognition.
  • Traditional machine learning methods show limitations in accuracy and stability for real-world applications.
  • Improving sEMG-based gesture recognition is essential for advanced human-computer interaction.

Purpose of the Study:

  • To develop an improved method for gesture recognition using sEMG signals.
  • To enhance the accuracy and stability of sEMG-based gesture recognition systems.
  • To address the limitations of existing algorithms in practical application scenarios.

Main Methods:

  • Proposed a novel method combining feature selection and ensemble extreme learning machine (EELM).
Keywords:
extreme learning machinefeature selectiongesture recognitionmachine learningsEMG signal

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  • Utilized recursive feature elimination (RFE) for optimal feature selection from preprocessed sEMG signals.
  • Employed majority voting to integrate predictions from multiple ELM base classifiers.

    • Main Results:

    • The proposed EELM method achieved the best performance with an overall average accuracy of 77.9%.
    • Demonstrated superior performance compared to Decision Tree (DT), Extreme Learning Machine (ELM), and Random Forest (RF) algorithms.
    • Successfully evaluated on the Ninapro DB5 dataset with 52 hand movements from 10 subjects.

    Conclusions:

    • The combination of feature selection and EELM significantly improves sEMG-based gesture recognition.
    • The proposed method offers a more accurate and stable solution for practical gesture recognition applications.
    • This approach provides a promising advancement in the field of human-computer interaction via sEMG.