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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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An adaptive learning method for long-term gesture recognition based on surface electromyography.

Yurong Li1,2, Xiaofeng Lin1,2, Heng Lin1,2

  • 1College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, Fujian, People's Republic of China.

Physiological Measurement
|December 3, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for long-term gesture recognition using surface electromyography (EMG) signals. The approach ensures over 90% accuracy for different-day use, significantly improving prosthetic applications.

Keywords:
adaptive updategesture recognitionlong-term applicationsurface electromyography

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Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Signal Processing

Background:

  • Surface electromyography (EMG) signals are crucial for human-computer interaction but suffer from time-varying characteristics and electrode shift, hindering long-term gesture recognition.
  • Existing classification models trained on EMG data are often not applicable across different days, limiting the use of commercial prosthetics.

Purpose of the Study:

  • To develop an optimized method for long-term gesture recognition in EMG signal analysis.
  • To address the challenges of non-stationarity and electrode shift in EMG signals for reliable, multi-day gesture classification.

Main Methods:

  • Extracted differential common spatial patterns (CSP) features for robust signal representation.
  • Applied non-negative matrix factorization (NMF) for dimensionality reduction to mitigate non-stationarity.
  • Utilized a clustering and classification self-training scheme for adaptive model updates using unlabeled data.

Main Results:

  • Achieved over 90% accuracy in gesture recognition across 30 days, comparable to daily calibration with labeled data.
  • Demonstrated the method's effectiveness with minimal unlabeled gesture samples for daily model updates.
  • Validated the feasibility of the proposed long-term gesture recognition scheme on a comprehensive dataset.

Conclusions:

  • The proposed method ensures superior performance and significantly simplifies daily use for long-term EMG-based gesture recognition.
  • This approach is highly suitable for practical, long-term applications such as advanced prosthetic devices.
  • Optimized feature extraction, dimensionality reduction, and adaptive calibration are key to overcoming daily variations in EMG signals.