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Improving the Robustness of Human-Machine Interactive Control for Myoelectric Prosthetic Hand During Arm Position

Ang Ke1, Jian Huang1,2, Jing Wang1

  • 1Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China.

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|June 24, 2022
PubMed
Summary

This study enhances prosthetic hand control by improving grasp classification accuracy using combined electromyography (EMG) and force myography (FMG) signals. A novel sequential decision algorithm boosts performance during dynamic arm movements.

Keywords:
EMG-FMG controlarm movementgesture recognitionpost-processingrobustness

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Area of Science:

  • Biomedical Engineering
  • Robotics
  • Human-Computer Interaction

Background:

  • Electromyography (EMG) based hand grasp classification faces challenges in prosthetic control due to dynamic arm position changes.
  • Existing methods often lack robustness when arm posture varies during natural hand actions.

Purpose of the Study:

  • To develop a robust framework for classifying hand grasp types during dynamic arm movements.
  • To improve prosthetic hand control by enhancing classification accuracy and reliability.

Main Methods:

  • Implemented a multi-modal sensing strategy using co-located synchronous electromyography (EMG) and force myography (FMG) signals.
  • Developed a sequential decision algorithm combining a Recurrent Neural Network (RNN) deep learning model with a knowledge-based post-processing model.

Main Results:

  • Multi-modal EMG-FMG signals achieved over 10% higher classification accuracy compared to EMG-only signals.
  • The proposed sequential decision algorithm improved accuracy by over 4% compared to baseline models using EMG-FMG data.

Conclusions:

  • The combined EMG-FMG approach significantly enhances hand grasp classification robustness.
  • The novel sequential decision algorithm offers a more accurate and reliable method for prosthetic hand control during dynamic arm movements.