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Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand
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Force Myography to Control Robotic Upper Extremity Prostheses: A Feasibility Study.

Erina Cho1, Richard Chen1, Lukas-Karim Merhi1

  • 1MENRVA Research Group, School of Engineering Science, Simon Fraser University , Burnaby, BC , Canada.

Frontiers in Bioengineering and Biotechnology
|March 26, 2016
PubMed
Summary
This summary is machine-generated.

Force myography (FMG) shows promise for controlling advanced robotic prostheses. This study found FMG can accurately detect user intentions for multiple grips, improving prosthetic hand functionality for amputees.

Keywords:
classificationforce myographyforce sensing resistorsgripresidual limbtransradial amputee

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Human-Machine Interfaces

Background:

  • Advancements in assistive technology have enabled multi-dexterous robotic prostheses for upper limb amputees.
  • Current control techniques for these prostheses have limited performance, hindering their widespread adoption.
  • Surface electromyography (sEMG) is the established method for detecting user intention, but alternatives are being explored.

Purpose of the Study:

  • To investigate Force Myography (FMG) as a viable alternative to sEMG for controlling upper extremity robotic prostheses.
  • To evaluate the efficacy of FMG in decoding user intentions for various hand grips using a commercial robotic hand.

Main Methods:

  • The study involved four male subjects with transradial amputations.
  • A protocol was designed to assess FMG's prediction accuracy for up to 11 different grips on the Bebionic3 robotic hand.
  • Grip classification accuracy was evaluated using combinations of 6 to 11 grips.

Main Results:

  • Force myography achieved over 70% accuracy in classifying six essential grips for daily activities using residual limb signals.
  • Implementing additional control strategies, such as utilizing the Bebionic3's available modes, significantly improved classification accuracy.
  • Classification accuracy reached up to 88.83% for opposed thumb grips and 89.00% for non-opposed thumb grips.

Conclusions:

  • Force myography presents a promising non-invasive approach for controlling advanced prosthetic hands.
  • FMG demonstrates potential to enhance the functionality and usability of upper extremity prostheses.
  • Improved grip classification accuracy using FMG can lead to more intuitive and effective prosthetic control for amputees.