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Newton-method based iterative learning control for robot-assisted rehabilitation using FES

Newton-method based iterative learning control for robot-assisted rehabilitation using FES
Newton-method based iterative learning control for robot-assisted rehabilitation using FES
Precise control of useful movement is critical in providing effective upper limb stroke rehabilitation using functional electrical stimulation (FES). To address the lack of accuracy currently available in clinical practice, this paper develops a general framework based on iterative learning control, an advanced model-based approach that has been successfully employed in three clinical treatment trials. An upper limb model is first developed to encompass unconstrained movements of the upper arm, with, in line with clinical need, additional assistance incorporated via a general class of robotic support mechanism. An iterative learning scheme is then developed to enable a subset of joint angles to be controlled via stimulation of an arbitrary set of muscles. This scheme is the first ILC approach which explicitly addresses coupled multivariable nonlinear dynamics in rehabilitation, enforcing convergence over multiple repetitions of a reaching task. Experiments with six participants confirm practical utility and performance.
0957-4158
934-943
Freeman, C T
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Freeman, C T
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815

Freeman, C T (2014) Newton-method based iterative learning control for robot-assisted rehabilitation using FES. Mechatronics, 24, 934-943. (doi:10.1016/j.mechatronics.2014.04.001).

Record type: Article

Abstract

Precise control of useful movement is critical in providing effective upper limb stroke rehabilitation using functional electrical stimulation (FES). To address the lack of accuracy currently available in clinical practice, this paper develops a general framework based on iterative learning control, an advanced model-based approach that has been successfully employed in three clinical treatment trials. An upper limb model is first developed to encompass unconstrained movements of the upper arm, with, in line with clinical need, additional assistance incorporated via a general class of robotic support mechanism. An iterative learning scheme is then developed to enable a subset of joint angles to be controlled via stimulation of an arbitrary set of muscles. This scheme is the first ILC approach which explicitly addresses coupled multivariable nonlinear dynamics in rehabilitation, enforcing convergence over multiple repetitions of a reaching task. Experiments with six participants confirm practical utility and performance.

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Published date: 18 January 2014
Organisations: EEE

Identifiers

Local EPrints ID: 361355
URI: http://eprints.soton.ac.uk/id/eprint/361355
ISSN: 0957-4158
PURE UUID: 653cdc71-540b-4a7d-854b-e0bb36846508

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Date deposited: 18 Jan 2014 14:25
Last modified: 02 Dec 2019 20:46

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