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Using functional electrical stimulation mediated by iterative learning control and robotics to improve arm movement for people with Multiple Sclerosis

Using functional electrical stimulation mediated by iterative learning control and robotics to improve arm movement for people with Multiple Sclerosis
Using functional electrical stimulation mediated by iterative learning control and robotics to improve arm movement for people with Multiple Sclerosis
Abstract:
Few interventions address multiple sclerosis (MS) arm dysfunction but robotics and functional electrical stimulation (FES) appear promising. This paper investigates the feasibility of combining FES with passive robotic support during virtual reality (VR) training tasks to improve upper limb function in people with multiple sclerosis (pwMS). The system assists patients in following a specified trajectory path, employing an advanced model-based paradigm termed iterative learning control (ILC) to adjust the FES to improve accuracy and maximise voluntary effort. Reaching tasks were repeated six times with ILC learning the optimum control action from previous attempts. A convenience sample of five pwMS was recruited from local MS societies, and the intervention comprised 18 one-hour training sessions over 10 weeks. The accuracy of tracking performance without FES and the amount of FES delivered during training were analyzed using regression analysis. Clinical functioning of the arm was documented before and after treatment with standard tests. Statistically significant results following training included: improved accuracy of tracking performance both when assisted and unassisted by FES; reduction in maximum amount of FES needed to assist tracking; and less impairment in the proximal arm that was trained. The system was well tolerated by all participants with no increase in muscle fatigue reported. This study confirms the feasibility of FES combined with passive robot assistance as a potentially effective intervention to improve arm movement and control in pwMS and provides the basis for a follow-up study.
235 - 248
Sampson, P.
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Freeman, C.T.
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Coote, S.
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Demain, S.
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Feys, P.
a75d153d-64f1-406a-98ce-d40b36ce14bb
Meadmore, K. L.
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Hughes, A. -M.
11239f51-de47-4445-9a0d-5b82ddc11dea
Sampson, P.
58e15b2a-529e-4273-8ff0-47f1a17e6bd1
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Coote, S.
2519dbf5-95e7-42fc-922e-e2f204a49a59
Demain, S.
09b1124d-750a-4eb1-90c7-91f5f222fc31
Feys, P.
a75d153d-64f1-406a-98ce-d40b36ce14bb
Meadmore, K. L.
4b63707b-4c44-486c-958e-e84645e7ed33
Hughes, A. -M.
11239f51-de47-4445-9a0d-5b82ddc11dea

Sampson, P., Freeman, C.T., Coote, S., Demain, S., Feys, P., Meadmore, K. L. and Hughes, A. -M. (2016) Using functional electrical stimulation mediated by iterative learning control and robotics to improve arm movement for people with Multiple Sclerosis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24 (2), 235 - 248. (doi:10.1109/TNSRE.2015.2413906).

Record type: Article

Abstract

Abstract:
Few interventions address multiple sclerosis (MS) arm dysfunction but robotics and functional electrical stimulation (FES) appear promising. This paper investigates the feasibility of combining FES with passive robotic support during virtual reality (VR) training tasks to improve upper limb function in people with multiple sclerosis (pwMS). The system assists patients in following a specified trajectory path, employing an advanced model-based paradigm termed iterative learning control (ILC) to adjust the FES to improve accuracy and maximise voluntary effort. Reaching tasks were repeated six times with ILC learning the optimum control action from previous attempts. A convenience sample of five pwMS was recruited from local MS societies, and the intervention comprised 18 one-hour training sessions over 10 weeks. The accuracy of tracking performance without FES and the amount of FES delivered during training were analyzed using regression analysis. Clinical functioning of the arm was documented before and after treatment with standard tests. Statistically significant results following training included: improved accuracy of tracking performance both when assisted and unassisted by FES; reduction in maximum amount of FES needed to assist tracking; and less impairment in the proximal arm that was trained. The system was well tolerated by all participants with no increase in muscle fatigue reported. This study confirms the feasibility of FES combined with passive robot assistance as a potentially effective intervention to improve arm movement and control in pwMS and provides the basis for a follow-up study.

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Accepted/In Press date: 9 February 2015
e-pub ahead of print date: 24 March 2015
Published date: February 2016
Organisations: Physical & Rehabilitation Health, EEE

Identifiers

Local EPrints ID: 364800
URI: http://eprints.soton.ac.uk/id/eprint/364800
PURE UUID: a8eedd58-6862-4ebb-9dde-8e79f3c155a0
ORCID for K. L. Meadmore: ORCID iD orcid.org/0000-0001-5378-8370
ORCID for A. -M. Hughes: ORCID iD orcid.org/0000-0002-3958-8206

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Date deposited: 13 May 2014 09:27
Last modified: 07 Oct 2020 05:01

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Contributors

Author: P. Sampson
Author: C.T. Freeman
Author: S. Coote
Author: S. Demain
Author: P. Feys
Author: K. L. Meadmore ORCID iD
Author: A. -M. Hughes ORCID iD

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