Learning control in robot-assisted rehabilitation of motor skills—a review

Zhou, Shou-Han, Fong, Justin, Crocher, Vincent, Tan, Ying, Oetomo, Denny, and Mareels, Iven (2016) Learning control in robot-assisted rehabilitation of motor skills—a review. Journal of Control and Decision, 3 (1). pp. 19-43.

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Abstract

The key idea in iterative learning control is captured by the intuition of 'practice makes perfect'. The underlying learning is based on a gradient descent algorithm iteratively optimising an appropriate input–output measured criterion. How this paradigm is used to model quantitatively, at an input/output level, the learning that happens in the context of human motor skill learning is discussed in this note. Experimental studies of human motor learning, in robotically controlled environments, indicate that a model consisting of a classical (iterative) learning control augmented with an appropriate kinematic model of human motor motion fits the observed human learning behaviour well. In the context of the rehabilitation of motor skills, such models promise better human–machine interfaces that extend the capability and capacity of rehabilitation clinicians by creating effective robot–patient–clinician feedback loops. The economic promise of robot-assisted rehabilitation is to greatly extend the intervention capacity above what presently can be achieved by rehabilitation systems: addressing the needs of more people, over longer periods of time and at a distance in the comfort of their own personal environment. Moreover, the robot platforms provide for a more rigorous and quantitative evaluation of the patient’s motor skill across the entire personal rehabilitation trajectory, which opens up opportunities for improved, more individually tuned rehabilitation regimes.

Item ID: 68408
Item Type: Article (Research - C1)
ISSN: 2330-7714
Copyright Information: © 2016 The Authors(s). Published by Taylor & Francis. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution,and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in anyway.
Date Deposited: 21 Jul 2021 02:38
FoR Codes: 52 PSYCHOLOGY > 5204 Cognitive and computational psychology > 520499 Cognitive and computational psychology not elsewhere classified @ 30%
40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400705 Control engineering @ 60%
40 ENGINEERING > 4003 Biomedical engineering > 400310 Rehabilitation engineering @ 10%
SEO Codes: 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 50%
28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280121 Expanding knowledge in psychology @ 50%
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