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A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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Highly Responsive Robotic Prosthetic Hand Control Considering Electrodynamic Delay.

Jiwoong Won1, Masami Iwase1

  • 1Department of Robotics and Mechatronics, Tokyo Denki University, Tokyo 120-8551, Japan.

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|January 11, 2025
PubMed
Summary

This study introduces an improved control system for myoelectric prostheses, reducing response time by considering Electromechanical Delay (EMD) in Electromyography (EMG) signals. The new system enhances accuracy and responsiveness for more practical prosthetic limb control.

Keywords:
NARX modelelectro-mechanical delay (EMD)electromyography (EMG)user’s intentionzero-phase error tracking control (ZPETC)

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Area of Science:

  • Robotics and Human-Machine Interaction
  • Biomedical Engineering
  • Control Systems

Background:

  • The integration of robots into society necessitates advanced human-machine interfaces.
  • Myoelectric prostheses rely on Electromyography (EMG) signals, which are affected by Electromechanical Delay (EMD).
  • Existing control systems often lack accuracy and speed, particularly for complex movements.

Purpose of the Study:

  • To develop a faster and more accurate control system for myoelectric prostheses.
  • To incorporate Electromechanical Delay (EMD) into the control system design.
  • To enhance the system's capability for complex finger movements.

Main Methods:

  • Replaced a 4-channel wired EMG sensor with an 8-channel wireless EMG sensor for improved convenience and data acquisition.
  • Analyzed communication delays associated with the wireless sensor and validated EMD utilization.
  • Proposed a MISO-NARX model to overcome limitations of the SISO-NARX model and introduced ridge regression for improved system identification accuracy with more EMG channels.
  • Implemented a ZPETC+PID controller with an actual servo motor.

Main Results:

  • The enhanced control system demonstrated a significant reduction in response time, reaching the target value approximately 0.240 seconds faster than the baseline 0.428 seconds.
  • The use of an 8-channel wireless EMG sensor improved user convenience and data channel availability.
  • Ridge regression successfully addressed model complexity and accuracy issues arising from increased EMG channels, enhancing system identification.
  • The ZPETC+PID controller effectively improved the performance of the servo motor.

Conclusions:

  • The developed control system significantly enhances the responsiveness and accuracy of myoelectric prostheses.
  • The integration of EMD analysis and advanced modeling techniques (MISO-NARX, ridge regression) is crucial for improving prosthetic control.
  • This research paves the way for more practical and sophisticated myoelectric prosthetic devices.