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Iterative learning control for stroke rehabilitation with input dependent muscle fatigue modeling

Iterative learning control for stroke rehabilitation with input dependent muscle fatigue modeling
Iterative learning control for stroke rehabilitation with input dependent muscle fatigue modeling
The consequences of a stroke is a major and increasing problem world wide. Many people who suffer a stroke are left with permanent impairment but the possibility exists that suitable rehabilitation could increase mobility and, for example, enable independent living. This, in turn, requires effective rehabilitation where it is known that currently available methods are relatively poor and are not well suited to home use, where the latter aspect is critical to improving practice and reducing costs. An accepted method to relearn lost function, such as reaching out to an object, is repeated attempts with learning from previous from those already completed with the application of applied stimulation if required. This requirement is analogous to iterative learning control and much progress, with supporting clinical trials data, has been reported on using this engineering design method to regulate the applied stimulation such that patient improvement in completing the task corresponds to increasing voluntary input and reduced stimulation. The applied stimulation in this application can induce muscle fatigue and this paper gives new result on enhancing the control laws to mitigate this unwanted effect.
IEEE
Luijten, Fons
a402602f-d440-496c-94b5-ddcb54432662
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Luijten, Fons
a402602f-d440-496c-94b5-ddcb54432662
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72

Luijten, Fons, Chu, Bing and Rogers, Eric (2018) Iterative learning control for stroke rehabilitation with input dependent muscle fatigue modeling. In Proceedings of American Control Conference (ACC) 2018. IEEE. 6 pp . (doi:10.23919/ACC.2018.8431755).

Record type: Conference or Workshop Item (Paper)

Abstract

The consequences of a stroke is a major and increasing problem world wide. Many people who suffer a stroke are left with permanent impairment but the possibility exists that suitable rehabilitation could increase mobility and, for example, enable independent living. This, in turn, requires effective rehabilitation where it is known that currently available methods are relatively poor and are not well suited to home use, where the latter aspect is critical to improving practice and reducing costs. An accepted method to relearn lost function, such as reaching out to an object, is repeated attempts with learning from previous from those already completed with the application of applied stimulation if required. This requirement is analogous to iterative learning control and much progress, with supporting clinical trials data, has been reported on using this engineering design method to regulate the applied stimulation such that patient improvement in completing the task corresponds to increasing voluntary input and reduced stimulation. The applied stimulation in this application can induce muscle fatigue and this paper gives new result on enhancing the control laws to mitigate this unwanted effect.

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Iterative learning control for stroke rehabilitation with input dependent - Accepted Manuscript
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Accepted/In Press date: 20 January 2018
e-pub ahead of print date: 16 August 2018
Venue - Dates: American Control Conference 2018: ACC 2018, United States, 2018-06-27 - 2018-06-29

Identifiers

Local EPrints ID: 418179
URI: http://eprints.soton.ac.uk/id/eprint/418179
PURE UUID: 66f2abbf-2d5c-4fc1-9f69-27167d9d004f
ORCID for Bing Chu: ORCID iD orcid.org/0000-0002-2711-8717
ORCID for Eric Rogers: ORCID iD orcid.org/0000-0003-0179-9398

Catalogue record

Date deposited: 23 Feb 2018 17:30
Last modified: 07 Oct 2020 05:20

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Contributors

Author: Fons Luijten
Author: Bing Chu ORCID iD
Author: Eric Rogers ORCID iD

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