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Functional Electrical Stimulation mediated by Iterative Learning Control and 3D robotics reduces motor impairment in chronic stroke

Functional Electrical Stimulation mediated by Iterative Learning Control and 3D robotics reduces motor impairment in chronic stroke
Functional Electrical Stimulation mediated by Iterative Learning Control and 3D robotics reduces motor impairment in chronic stroke
Background: Novel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients’ voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort. Methods: Five hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants’ arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC) algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test) at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression. Results: From baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced. Conclusions: The concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this.
1-11
Meadmore, K L
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Hughes, A-M
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Freeman, C T
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Cai, Z
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Tong, D
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Burridge, J H
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Rogers, E
611b1de0-c505-472e-a03f-c5294c63bb72
Meadmore, K L
4b63707b-4c44-486c-958e-e84645e7ed33
Hughes, A-M
11239f51-de47-4445-9a0d-5b82ddc11dea
Freeman, C T
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Cai, Z
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Tong, D
a83e5a96-c049-4b06-8078-7eaf16bc7468
Burridge, J H
9daa054b-c8b5-4306-a1b7-1424b466bade
Rogers, E
611b1de0-c505-472e-a03f-c5294c63bb72

Meadmore, K L, Hughes, A-M, Freeman, C T, Cai, Z, Tong, D, Burridge, J H and Rogers, E (2012) Functional Electrical Stimulation mediated by Iterative Learning Control and 3D robotics reduces motor impairment in chronic stroke. Journal of NeuroEngineering and Rehabilitation, 9 (32), 1-11.

Record type: Article

Abstract

Background: Novel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients’ voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort. Methods: Five hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants’ arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC) algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test) at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression. Results: From baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced. Conclusions: The concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this.

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Published date: 2012
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 273120
URI: http://eprints.soton.ac.uk/id/eprint/273120
PURE UUID: 1c056e78-d8f7-404c-863f-28605989cc65
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
ORCID for E Rogers: ORCID iD orcid.org/0000-0003-0179-9398

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Date deposited: 18 Jan 2012 14:17
Last modified: 07 Oct 2020 05:03

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