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Permanent link (DOI): https://doi.org/10.7939/R3CF9JJ2X

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Towards Prosthetic Arms as Wearable Intelligent Robots Open Access

Descriptions

Other title
Subject/Keyword
electromyography
artificial intelligence
prediction
reinforcement learning
intelligent agents
collaborative control
robotics
prosthetics
general value functions
myoelectric control
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Sherstan, Craig
Supervisor and department
Sutton, Richard S. (Computing Science)
Pilarski, Patrick M. (Medicine)
Examining committee member and department
Pilarski, Patrick M. (Medicine)
Jones, Kelvin (Physical Education and Recreation)
Sutton, Richard S. (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2015-09-14T15:42:57Z
Graduation date
2015-11
Degree
Master of Science
Degree level
Master's
Abstract
The control of powered prosthetic arms has been researched for over 50 years, yet prosthetic control remains an open problem, not just from a research perspective, but from a clinical perspective as well. Significant advances have been made in the manufacture of highly functional prosthetic limbs, yet the control of such limbs remains largely impractical. The core issue is that there is a significant mismatch between the number of functions available in modern powered prosthetic arms and the number of functions an amputee can actively attend to at any given moment. One approach to addressing this mismatch is the idea of treating the arm as an intelligent, goal-seeking agent - such an agent can learn from its experience and adapt its actions to improve its ability to accomplish a goal. It is hypothesized that such intelligent agents will be able to compensate for the existing limitations in the communication bandwidth between a powered prosthetic arm and an amputee. The work of this thesis looks at several steps towards building such agency into a prosthetic arm, including pattern recognition methods, compound predictions, and collaborative control between the arm and the user. Essentially, this body of work looks at ways of understanding the user's desires, as measured in various ways, such as desired movements, or expected future joint angles, and controlling the arm so as to achieve those desires. The first contribution of this thesis is the identification of a scenario under which current pattern recognition approaches to prosthetic control do not generalize well. The second contribution is the demonstration that it is possible to layer predictors, known as general value functions, and that such layering can improve feature representation and predictive power. Finally, this thesis demonstrates a method for improving the control of a prosthetic arm using a collaborative control method that learns predictions of user behavior which are then used to assist in controlling the arm. In the long term, the methods and philosophy to prosthetic control explored in this thesis may greatly improve an amputee's ability to control their prosthesis. Further, this approach may be extended to other domains of human-machine interaction where there is a mismatch between the number of functions in a system and the user's ability to attend to those functions, such as smart phones, computers and teleoperated robots.
Language
English
DOI
doi:10.7939/R3CF9JJ2X
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Sherstan, C., Pilarski, P. M., “Multilayer General Value Functions for Robotic Prediction and Control”, IROS 2014 Workshop on AI and Robotics, IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, Illinois, September 14-18, 2014Sherstan, C., Modayil, J., and Pilarski, P. M., “A Collaborative Approach to Effecting Simultaneous Multi-joint Control of a Prosthetic Arm”, International Conference on Rehabilitation Robotics (ICORR), Singapore, August 11-14, 2015

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File title: Towards Prosthetic Arms as Wearable Intelligent Robots
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