Identification of the Dynamics of Human Arms after Stroke
Identification of the Dynamics of Human Arms after Stroke
The design of controllers to enable the application of Functional Electrical Stimulation as part of a rehabilitation programme for stroke patients requires an accurate model of electrically stimulated muscle. In this paper, nonlinear dynamics of the electrically stimulated muscle under isometric conditions is investigated, leading to the requirement to identify a Hammerstein model structure. Here we develop a two-stage identification method based on a preliminary construction of the linear part that is used as an initial estimate. Then the two-stage method is applied to identify the nonlinear part and optimize the linear part. The traditional ramp deconvolution method are implemented here and compared with the proposed method using a simulated muscle system that is based on experimental data from stroke patients. The results show that the proposed method outperforms the other one when implemented on the simulated muscle system with different noise levels.
Le, Fengmin
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Markovsky, Ivan
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Freeman, Christopher
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Rogers, Eric
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Le, Fengmin
3e44aa4d-33ea-4697-a9c2-b2bff35cee3c
Markovsky, Ivan
7d632d37-2100-41be-a4ff-90b92752212c
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Rogers, Eric
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Le, Fengmin, Markovsky, Ivan, Freeman, Christopher and Rogers, Eric
(2008)
Identification of the Dynamics of Human Arms after Stroke.
23rd IAR Workshop on Advanced Control and Diagnosis, Coventry, United Kingdom.
27 - 28 Nov 2008.
(Submitted)
Record type:
Conference or Workshop Item
(Paper)
Abstract
The design of controllers to enable the application of Functional Electrical Stimulation as part of a rehabilitation programme for stroke patients requires an accurate model of electrically stimulated muscle. In this paper, nonlinear dynamics of the electrically stimulated muscle under isometric conditions is investigated, leading to the requirement to identify a Hammerstein model structure. Here we develop a two-stage identification method based on a preliminary construction of the linear part that is used as an initial estimate. Then the two-stage method is applied to identify the nonlinear part and optimize the linear part. The traditional ramp deconvolution method are implemented here and compared with the proposed method using a simulated muscle system that is based on experimental data from stroke patients. The results show that the proposed method outperforms the other one when implemented on the simulated muscle system with different noise levels.
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Submitted date: November 2008
Additional Information:
Event Dates: 27-28 November 2008
Venue - Dates:
23rd IAR Workshop on Advanced Control and Diagnosis, Coventry, United Kingdom, 2008-11-27 - 2008-11-28
Organisations:
EEE, Southampton Wireless Group
Identifiers
Local EPrints ID: 266916
URI: http://eprints.soton.ac.uk/id/eprint/266916
PURE UUID: d3302280-6f78-4ec9-a52d-996605ae140c
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Date deposited: 14 Nov 2008 22:00
Last modified: 15 Mar 2024 02:42
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Contributors
Author:
Fengmin Le
Author:
Ivan Markovsky
Author:
Christopher Freeman
Author:
Eric Rogers
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