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Identification of electrically stimulated muscle after stroke

Identification of electrically stimulated muscle after stroke
Identification of electrically stimulated muscle after stroke
Stroke affects a large percentage of the population in UK and one of the most devastating and common consequences of the stroke is loss of the use of the arm and hand. Currently there is increasing interest in the application of control schemes as part of a rehabilitation programme for
survivors of a stroke. Functional Electrical Stimulation is applied, together with the model-based controller in order to ensure that the assistance provided coincides as much as possible with the patient’s voluntary intention. The difficulty encountered is lack of a reliable model of electrically stimulated muscle. Motivated by this, this thesis focus on identification of electrically stimulated
muscle, especially the impaired arm after stroke.

After studying the muscle behaviors and reviewing the existing muscle models, Hammerstein structure is chosen to model the nonlinear dynamics of the electrically stimulated muscle under isometric conditions. Firstly, batch identification algorithms are considered. A two-stage
algorithm is proposed, together with its identification procedure and comparison results on a stimulated muscle system. Due to its simple implementation and good performance, this algorithm has been developed to the later two iterative algorithms. Experimental results are used to
demonstrate the superior performance of the algorithms and the model structure when compared with others.

Further more, considering the slowly time-varying properties of the muscle system, recursive identification of Hammerstein structure is investigated later in the thesis. A novel recursive identification algorithm is developed, where the linear and nonlinear parameters are separated
and estimated recursively in a parallel manner, with each updating algorithm using the most up-to-date estimation produced by the other algorithm at each time instant. When compared with the leading technique involving over-parametrization together with a Recursive Least Squares
algorithm on numerical examples and experimental data, the proposed algorithm exhibits superior performance.

Finally, the identified muscle models have been used in FES control schemes for electrically stimulated muscle under isometric conditions and iterative learning controllers will be used since the repeated nature of the task. Besides the two nonlinear ILC approaches, several trialdependent
and adaptive control schemes has been designed and implemented in the thesis
Le, Fengmin
22ea919e-ceaa-43c3-9192-cc53d18a2a14
Le, Fengmin
22ea919e-ceaa-43c3-9192-cc53d18a2a14
Markovsky, Ivan
7d632d37-2100-41be-a4ff-90b92752212c
Freeman, C.T.
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815

Le, Fengmin (2011) Identification of electrically stimulated muscle after stroke. University of Southampton, School of Electronics and Computer Science, Doctoral Thesis, 127pp.

Record type: Thesis (Doctoral)

Abstract

Stroke affects a large percentage of the population in UK and one of the most devastating and common consequences of the stroke is loss of the use of the arm and hand. Currently there is increasing interest in the application of control schemes as part of a rehabilitation programme for
survivors of a stroke. Functional Electrical Stimulation is applied, together with the model-based controller in order to ensure that the assistance provided coincides as much as possible with the patient’s voluntary intention. The difficulty encountered is lack of a reliable model of electrically stimulated muscle. Motivated by this, this thesis focus on identification of electrically stimulated
muscle, especially the impaired arm after stroke.

After studying the muscle behaviors and reviewing the existing muscle models, Hammerstein structure is chosen to model the nonlinear dynamics of the electrically stimulated muscle under isometric conditions. Firstly, batch identification algorithms are considered. A two-stage
algorithm is proposed, together with its identification procedure and comparison results on a stimulated muscle system. Due to its simple implementation and good performance, this algorithm has been developed to the later two iterative algorithms. Experimental results are used to
demonstrate the superior performance of the algorithms and the model structure when compared with others.

Further more, considering the slowly time-varying properties of the muscle system, recursive identification of Hammerstein structure is investigated later in the thesis. A novel recursive identification algorithm is developed, where the linear and nonlinear parameters are separated
and estimated recursively in a parallel manner, with each updating algorithm using the most up-to-date estimation produced by the other algorithm at each time instant. When compared with the leading technique involving over-parametrization together with a Recursive Least Squares
algorithm on numerical examples and experimental data, the proposed algorithm exhibits superior performance.

Finally, the identified muscle models have been used in FES control schemes for electrically stimulated muscle under isometric conditions and iterative learning controllers will be used since the repeated nature of the task. Besides the two nonlinear ILC approaches, several trialdependent
and adaptive control schemes has been designed and implemented in the thesis

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More information

Published date: April 2011
Organisations: University of Southampton

Identifiers

Local EPrints ID: 185937
URI: http://eprints.soton.ac.uk/id/eprint/185937
PURE UUID: 748290c9-6aa2-4853-b211-9f95c738da97

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Date deposited: 24 May 2011 08:55
Last modified: 29 Jan 2020 14:31

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