title: Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching.
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.
  
  
  OBJECTIVE
  The goal of this study was to compare two switching-based methods of controlling a myoelectric arm: non-adaptive (or conventional) control and adaptive control (involving real-time prediction learning).
  
  
  METHODS
  Case series study.
  
  
  METHODS
  We compared non-adaptive and adaptive control in two different experiments. In the first, one amputee and one non-amputee subject controlled a robotic arm to perform a simple task; in the second, three able-bodied subjects controlled a robotic arm to perform a more complex task. For both tasks, we calculated the mean time and total number of switches between robotic arm functions over three trials.
  
  
  RESULTS
  Adaptive control significantly decreased the number of switches and total switching time for both tasks compared with the conventional control method.
  
  
  CONCLUSIONS
  Real-time prediction learning was successfully used to improve the control interface of a myoelectric robotic arm during uninterrupted use by an amputee subject and able-bodied subjects.
  
  
  CONCLUSIONS
  Adaptive control using real-time prediction learning has the potential to help decrease both the time and the cognitive load required by amputees in real-world functional situations when using myoelectric prostheses.
authors.affiliation: Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada Division of Physical Medicine & Rehabilitation, University of Alberta, Edmonton, AB, Canada.
authors.forename: Ann L
authors.initials: AL
authors.lastname: Edwards
authors.affiliation: Glenrose Rehabilitation Hospital, Alberta Health Services, Edmonton, AB, Canada.
authors.forename: Michael R
authors.initials: MR
authors.lastname: Dawson
authors.affiliation: Division of Physical Medicine & Rehabilitation, University of Alberta, Edmonton, AB, Canada Glenrose Rehabilitation Hospital, Alberta Health Services, Edmonton, AB, Canada.
authors.forename: Jacqueline S
authors.initials: JS
authors.lastname: Hebert
authors.affiliation: Division of Physical Medicine & Rehabilitation, University of Alberta, Edmonton, AB, Canada Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
authors.forename: Craig
authors.initials: C
authors.lastname: Sherstan
authors.affiliation: Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
authors.forename: Richard S
authors.initials: RS
authors.lastname: Sutton
authors.affiliation: Division of Physical Medicine & Rehabilitation, University of Alberta, Edmonton, AB, Canada Glenrose Rehabilitation Hospital, Alberta Health Services, Edmonton, AB, Canada.
authors.forename: K Ming
authors.initials: KM
authors.lastname: Chan
authors.affiliation: Division of Physical Medicine & Rehabilitation, University of Alberta, Edmonton, AB, Canada patrick.pilarski@ualberta.ca.
authors.forename: Patrick M
authors.initials: PM
authors.lastname: Pilarski
mesh_terms: D000671:Amputation, Surgical / Q000534:rehabilitation*; D001132:Arm; D001186:Artificial Limbs; D004576:Electromyography; D006801:Humans; D000069550:Machine Learning; D011474:Prosthesis Design; D012371:Robotics; D013647:Task Performance and Analysis
keywords: Upper limb prosthetics; prosthetic design; prosthetics; rehabilitation; rehabilitation of amputees