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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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A Systematic Study on Electromyography-Based Hand Gesture Recognition for Assistive Robots Using Deep Learning and

Pranesh Gopal1, Amandine Gesta2, Abolfazl Mohebbi2

  • 1Manipal Academy of Higher Education, Manipal 576104, India.

Sensors (Basel, Switzerland)
|May 28, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning and deep learning models significantly improve robotic hand prosthesis control for transradial amputees using surface electromyography (sEMG) signals. These advanced algorithms enhance motion intention classification, offering greater autonomy for individuals with upper limb loss.

Keywords:
assistive robotsdeep learningelectromyographygesture recognitionmachine learningmotion classificationprosthesistransradial amputation

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Area of Science:

  • Robotics
  • Biomedical Engineering
  • Machine Learning

Background:

  • Upper limb amputation significantly impacts daily living and autonomy.
  • Robotic hand prostheses offer improved functionality but face challenges in user control and adaptation.
  • Surface electromyography (sEMG) is a key sensing technology for myoelectric prostheses, yet signal noise and computational demands complicate its use.

Purpose of the Study:

  • To develop and evaluate motion intention classifiers for transradial amputees using machine learning and deep learning models.
  • To systematically analyze the impact of time-domain features and pre-processing parameters on classifier performance.
  • To investigate the influence of amputation level and conditions on classifier generalization.

Main Methods:

  • Implementation of various machine learning and deep learning models for EMG-based motion intention classification.
  • Benchmarking classifier performance based on generalization across different classes.
  • Systematic study on the effect of sliding window variations on feature-based and non-feature-based classification models.

Main Results:

  • Ensemble learning and deep learning algorithms demonstrated superior performance compared to classical machine learning methods.
  • The study identified correlations between sliding window trends and amputation levels.
  • Classifier performance varied based on individual amputation history and conditions, highlighting the need for personalized approaches.

Conclusions:

  • Machine learning and deep learning approaches show significant promise for enhancing the control of robotic hand prostheses.
  • Understanding the impact of features, pre-processing, and amputation characteristics is crucial for developing effective assistive robotic systems.
  • These findings are vital for advancing the development of intelligent, user-friendly prosthetic devices for amputees.