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Related Experiment Video

Updated: May 25, 2026

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

Multigrasp myoelectric control for a transradial prosthesis.

Skyler A Dalley1, Huseyin Atakan Varol, Michael Goldfarb

  • 1Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37235, USA. skyler.a.dalley@vanderbilt.edu

IEEE ... International Conference on Rehabilitation Robotics : [Proceedings]
|January 26, 2012
PubMed
Summary

This study introduces a new multigrasp myoelectric controller for transradial prostheses, enabling nine distinct hand postures via EMG signals. While effective, its transition speed is slower than the natural hand.

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Last Updated: May 25, 2026

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Therapy Interventions for Upper Limb Amputees Undergoing Selective Nerve Transfers
07:59

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Published on: October 29, 2021

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Human-Computer Interaction

Background:

  • Transradial amputations necessitate advanced prosthetic solutions.
  • Myoelectric control offers intuitive prosthesis operation.
  • Existing systems often lack comprehensive grasp capabilities.

Purpose of the Study:

  • To design and experimentally verify a novel multigrasp myoelectric controller.
  • To enable proportional control of a transradial prosthesis through nine distinct postures.
  • To compare the controller's performance against the native hand's functionality.

Main Methods:

  • Developed a multigrasp myoelectric controller using two surface EMG electrodes.
  • Implemented direct and proportional control for nine prosthesis postures.
  • Recruited five healthy subjects to operate a virtual prosthesis.
  • Quantified real-time performance metrics and compared with dataglove data of the native hand.

Main Results:

  • The multigrasp myoelectric controller achieved a 99.2% transition rate.
  • Average transition time for the controller was 1.49 seconds.
  • The native hand with a dataglove demonstrated a faster average transition time of 0.81 seconds.

Conclusions:

  • The developed multigrasp myoelectric controller shows promise for advanced prosthesis control.
  • Further optimization is needed to match the speed of natural hand transitions.
  • The study provides a benchmark for future myoelectric controller development.