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Adaptive and Autonomous Switching: Shared Control of Powered Prosthetic Arms Using Reinforcement Learning

  • Author / Creator
    Edwards, Ann L
  • Powered prosthetic arms with numerous controllable functions (i.e., grip patterns or movable joints) can be challenging to operate. Gated control---a common control method for myoelectric arms and other human-machine interfaces---allows users to select a function by switching through a static list of possible functions. However, switching between many controllable functions often entails significant time and cognitive effort on the part of the user when performing tasks. One way to decrease the number of switching interactions required of a user is to shift greater autonomy to the prosthetic device, thereby sharing the burden of control between the human and the machine. Previous work has demonstrated that reinforcement learning (RL), and specifically general value functions (GVFs), has the potential to reduce the time and switching cost of gated control methods. In the current work, we extend previous studies by advancing an RL method termed adaptive switching for use during real time control of a prosthetic arm. Adaptive switching uses contextual factors to build up predictions about the use of functions during a task. Based on these predictions, adaptive switching will continually optimize and change the order in which functions are presented to the user during switching. We also combine adaptive switching with another machine learning control method, termed autonomous switching, to further decrease the number of manual switching interactions required of a user. Autonomous switching uses predictions, learned in real time through the use of GVFs, to switch automatically between functions for the user. Over the course of several studies, we collected results from subjects with and without amputations, performing simple and more challenging tasks with a myoelectric robot arm. As a first contribution of this thesis, we present results from work on adaptive switching, and show that it leads to a reduction in the total switching cost (both in terms of time and the total number of switches), an effect that is especially pronounced for experienced myoelectric arm users. For our second and third contributions, we describe our autonomous switching approach and demonstrate that it is able to both learn and subsequently unlearn to switch autonomously during ongoing use, a key requirement for maintaining human-centered shared control. We found that although autonomous switching decreases the number of user-initiated switches compared to conventional control, some work remains to be done to improve accuracy of predictions leading to a switch. We also show that the addition of feedback to the user can significantly improve the performance of autonomous switching. This work promises to help improve the gated control method for prosthetic arms, as well as other domains involving human-machine interaction---in particular, assistive or rehabilitative devices that require switching between different modes of operation such as exoskeletons and powered orthotics.

  • Subjects / Keywords
  • Graduation date
    2016-06
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R35Q4RX49
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Faculty of Rehabilitation Medicine
  • Specialization
    • Rehabilitation Science
  • Supervisor / co-supervisor and their department(s)
    • Hebert, Jacqueline (Medicine)
    • Pilarski, Patrick (Medicine)
  • Examining committee members and their departments
    • Bowling, Michael (Computing Science)