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Improving the Robustness of Electromyogram-Pattern Recognition for Prosthetic Control by a Postprocessing Strategy.

Xu Zhang1,2,3, Xiangxin Li1,4, Oluwarotimi Williams Samuel1,4

  • 1CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Frontiers in Neurorobotics
|October 13, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new postprocessing strategy for electromyogram (EMG) pattern recognition (PR) control of prosthetic limbs. The method significantly reduces errors by locking outputs during constant contractions, improving prosthetic control robustness.

Keywords:
amputeeelectromyogrammotion onset detectionmyoelectric prosthesispattern recognitionpostprocessingrehabilitation roboticsrobustness

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Area of Science:

  • Rehabilitation Robotics
  • Biomedical Engineering
  • Prosthetics Control

Background:

  • Electromyogram (EMG) pattern recognition (PR) offers advanced control for prosthetic limbs.
  • Clinical application is hindered by poor robustness against daily use interferences, leading to misclassifications.
  • Postprocessing methods can mitigate erroneous outputs by analyzing previous classifications.

Purpose of the Study:

  • To propose and investigate a novel postprocessing strategy for EMG-PR-based prosthetic control.
  • To enhance the robustness and reduce misclassifications in EMG-based motion intention detection.
  • To evaluate the strategy's effectiveness with different motion onset detectors.

Main Methods:

  • A postprocessing strategy was developed to lock outputs during constant contractions upon detecting motion onset.
  • Three motion onset detectors were tested: mean absolute value, Teager-Kaiser energy operator, and mechanomyogram (MMG).
  • The strategy's performance was evaluated in an online test comparing it to original classification outputs.

Main Results:

  • The proposed postprocessing strategy effectively suppressed erroneous outputs, especially during rest and constant contractions.
  • Using mechanomyogram (MMG) as the motion onset detector yielded the most significant performance improvement, reducing errors by approximately 50%.
  • The MMG-based approach demonstrated the highest robustness against changes in threshold values.

Conclusions:

  • The developed postprocessing strategy enhances the reliability of EMG-PR-based prosthetic control.
  • Mechanomyogram (MMG) shows promise as an effective motion onset detector for this strategy.
  • Further improvements may be achieved with smooth and responsive motion onset detectors, facilitating clinical adoption.