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Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and

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Summary

Myoelectric prostheses use muscle signals for limb control but face challenges in practical application. Advanced pattern recognition in electromyography (EMG) offers a promising solution for personalized upper limb prosthesis (ULP) control.

Keywords:
EMGMyo-prosthesisPattern recognitionmyosignals

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Machine Learning in Healthcare

Background:

  • Upper limb amputation significantly impacts daily activities, necessitating functional restoration.
  • Myoelectric prostheses aim to restore lost limb function using residual muscle signals.
  • Current myoelectric control systems face challenges in signal acquisition, computational demands, and individual variability.

Purpose of the Study:

  • To review the technical control aspects of upper limb prosthesis (ULP) inventions.
  • To examine the real-world applications and pattern recognition control of myoelectric prostheses in amputees.
  • To identify existing challenges and future research directions for improved prosthetic utility.

Main Methods:

  • Review of pattern recognition schemes for myoelectric prosthetic control systems.
  • Analysis of real-time applications and performance on amputees.
  • Discussion of factors affecting traditional electromyography (EMG)-pattern recognition methods.

Main Results:

  • Modified machine learning schemes show potential to mitigate factors affecting traditional EMG-pattern recognition.
  • Intelligent pattern recognition techniques can discriminate multiple degrees of freedom with high accuracy.
  • Real-world efficiency and accessibility of these advanced techniques in amputee applications require further investigation.

Conclusions:

  • Personalized and adaptive solutions are crucial for maximizing the utility of upper limb prostheses.
  • Pattern recognition offers a pathway to more intuitive and effective myoelectric control.
  • Further research is needed to bridge the gap between advanced techniques and practical, real-world amputee applications.