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A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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Confidence-based rejection for improved pattern recognition myoelectric control.

Erik J Scheme1, Bernard S Hudgins, Kevin B Englehart

  • 1Institute of Biomedical Engineering, University of NewBrunswick, Fredericton NBE3B 5A3, Canada. escheme@unb.ca

IEEE Transactions on Bio-Medical Engineering
|January 17, 2013
PubMed
Summary

This study introduces a novel myoelectric control system with motion rejection capabilities, improving accuracy and performance in target acquisition tasks for both able-bodied and amputee users.

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Engineering
  • Human-Computer Interaction

Background:

  • Myoelectric control systems are crucial for prosthetic devices.
  • Existing systems often struggle with motion artifacts and accuracy.
  • Linear Discriminant Analysis (LDA) is a common pattern recognition technique.

Purpose of the Study:

  • To develop and evaluate a novel myoelectric control scheme with motion rejection.
  • To enhance the accuracy and reliability of myoelectric control.
  • To assess the system's performance in a real-time target acquisition task.

Main Methods:

  • Developed a myoelectric control scheme extending Linear Discriminant Analysis (LDA) with a confidence score for motion rejection.
  • Implemented class-specific rejection thresholds.
  • Compared the proposed system against a baseline LDA system using a Fitts' law-based target acquisition task.

Main Results:

  • The proposed system demonstrated superior active motion classification accuracy compared to standard LDA across all rejection thresholds.
  • Myoelectric control using the rejection classifier adhered to Fitts' law with high coefficients of determination (R(2) > 0.943).
  • Significantly improved throughput, path efficiency, and completion rates (p < 0.001) were observed for both able-bodied and amputee subjects.

Conclusions:

  • The novel myoelectric control scheme effectively rejects motion artifacts, enhancing classification accuracy.
  • The rejection-capable system shows improved performance in target acquisition tasks, aligning with Fitts' law.
  • This advancement offers significant benefits for users of prosthetic devices and assistive technologies.