Multiple model iterative learning control of FES electrode arrays
Multiple model iterative learning control of FES electrode arrays
Stroke is a common cause of hand and upper limb disability, but current rehabilitation approaches do not adequately support successful recovery. Functional electrical stimulation (FES) is the most widely used assistive technology, and is able to support accurate hand and wrist motion when applied using multi-element electrode arrays. However, accurate movements have only been possible using an iterative learning control (ILC) approach involving many repeated model identification tests. This lengthy process limits wide-spread use. This paper presents a solution for FES electrode array control using estimation-based multiple-model ILC (EM-MILC), in which a set of parameterised models is used to automatically update the stimulation applied to each array element every time a task is carried out. This removes the need for model identification, significantly improving system usability whilst maintaining high performance. Experimental results demonstrate that EM-MILC reduces the average number of tests from 16 to 3, compared to the most accurate existing approach.
Iterative Learning Control, functional electrical stimulation, stroke rehabilitation, Multiple Model Switched Adaptive Control
Hodgins, Lucy
2cb70295-f4b0-4c0d-ba23-43fc531b9392
Freeman, Chris T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Belkhatir, Zehor
de90d742-a58f-4425-837c-20ff960fb9b6
November 2024
Hodgins, Lucy
2cb70295-f4b0-4c0d-ba23-43fc531b9392
Freeman, Chris T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Belkhatir, Zehor
de90d742-a58f-4425-837c-20ff960fb9b6
Hodgins, Lucy, Freeman, Chris T. and Belkhatir, Zehor
(2024)
Multiple model iterative learning control of FES electrode arrays.
21st International Conference on Informatics in Control, Automation and Robotics: ICINCO 2024, , Porto, Portugal.
18 - 20 Nov 2024.
8 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Stroke is a common cause of hand and upper limb disability, but current rehabilitation approaches do not adequately support successful recovery. Functional electrical stimulation (FES) is the most widely used assistive technology, and is able to support accurate hand and wrist motion when applied using multi-element electrode arrays. However, accurate movements have only been possible using an iterative learning control (ILC) approach involving many repeated model identification tests. This lengthy process limits wide-spread use. This paper presents a solution for FES electrode array control using estimation-based multiple-model ILC (EM-MILC), in which a set of parameterised models is used to automatically update the stimulation applied to each array element every time a task is carried out. This removes the need for model identification, significantly improving system usability whilst maintaining high performance. Experimental results demonstrate that EM-MILC reduces the average number of tests from 16 to 3, compared to the most accurate existing approach.
Text
ICINCO_conference_paper
- Accepted Manuscript
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Published date: November 2024
Venue - Dates:
21st International Conference on Informatics in Control, Automation and Robotics: ICINCO 2024, , Porto, Portugal, 2024-11-18 - 2024-11-20
Keywords:
Iterative Learning Control, functional electrical stimulation, stroke rehabilitation, Multiple Model Switched Adaptive Control
Identifiers
Local EPrints ID: 493127
URI: http://eprints.soton.ac.uk/id/eprint/493127
PURE UUID: 3b8c94f4-3a28-4885-9ca8-0d11eaac5332
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Date deposited: 23 Aug 2024 16:43
Last modified: 11 Dec 2024 02:39
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Contributors
Author:
Lucy Hodgins
Author:
Chris T. Freeman
Author:
Zehor Belkhatir
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