Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching.

Journal: Prosthetics and orthotics international
Published Date: Oct 1, 2016

Abstract

BACKGROUND: Myoelectric prostheses currently used by amputees can be difficult to control. Machine learning, and in particular learned predictions about user intent, could help to reduce the time and cognitive load required by amputees while operating their prosthetic device.

Authors

  • Ann L Edwards
    Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada Division of Physical Medicine & Rehabilitation, University of Alberta, Edmonton, AB, Canada.
  • Michael R Dawson
    Glenrose Rehabilitation Hospital, Alberta Health Services, Edmonton, AB, Canada.
  • Jacqueline S Hebert
    d Alberta Health Services , Glenrose Rehabilitation Hospital , Edmonton , Canada.
  • Craig Sherstan
    Division of Physical Medicine & Rehabilitation, University of Alberta, Edmonton, AB, Canada Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
  • Richard S Sutton
    Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
  • K Ming Chan
    Division of Physical Medicine & Rehabilitation, University of Alberta, Edmonton, AB, Canada Glenrose Rehabilitation Hospital, Alberta Health Services, Edmonton, AB, Canada.
  • Patrick M Pilarski
    Division of Physical Medicine & Rehabilitation, University of Alberta, Edmonton, AB, Canada patrick.pilarski@ualberta.ca.

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