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Surface electrode array-based electrical stimulation and iterative learning control for hand rehabilitation

Surface electrode array-based electrical stimulation and iterative learning control for hand rehabilitation
Surface electrode array-based electrical stimulation and iterative learning control for hand rehabilitation
This thesis addresses the use of surface electrode arrays to regulate the stimulation applied to the hand and wrist muscles in order to induce hand movement to desired posture. Electrode array-based electric stimulation is a relatively novel and promising rehabilitation technology, due to its potential to deliver selective stimulation signal to underlying muscles via chosen elements of the arrays. A general control strategy developed in this thesis embeds optimisation methods for selection of appropriate elements of the electrode array with iterative learning control. In iterative learning control, the patient makes repeated attempts to complete a predefined task with the aim of gradually decreasing the error between the movement performed and desired one. A number of different gradient-based methods, such as penalty method and sparse optimisation methods has been developed based on theoretical and experimental findings. These methods are used to find a sparse input vector, which is employed to select only those array elements that are critical to task completion within iterative learning control framework. Experimental results using multi-channel stimulation and 40 element surface electrode array confirm accurate tracking of selected hand postures. Based on the experimental results and the existing literature, a new system for the hand and wrist restoration has been designed. The key element of the system is a game-based task oriented training environment designed for a wide group of patients, including patients with spasticity and hemiplegia.
Soska, Anna
61bf571f-41d6-4352-9e72-8348255f5cd2
Soska, Anna
61bf571f-41d6-4352-9e72-8348255f5cd2
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Lewin, Paul
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e

Soska, Anna (2015) Surface electrode array-based electrical stimulation and iterative learning control for hand rehabilitation. University of Southampton, Faculty of Physical Sciences and Engineering, Doctoral Thesis, 155pp.

Record type: Thesis (Doctoral)

Abstract

This thesis addresses the use of surface electrode arrays to regulate the stimulation applied to the hand and wrist muscles in order to induce hand movement to desired posture. Electrode array-based electric stimulation is a relatively novel and promising rehabilitation technology, due to its potential to deliver selective stimulation signal to underlying muscles via chosen elements of the arrays. A general control strategy developed in this thesis embeds optimisation methods for selection of appropriate elements of the electrode array with iterative learning control. In iterative learning control, the patient makes repeated attempts to complete a predefined task with the aim of gradually decreasing the error between the movement performed and desired one. A number of different gradient-based methods, such as penalty method and sparse optimisation methods has been developed based on theoretical and experimental findings. These methods are used to find a sparse input vector, which is employed to select only those array elements that are critical to task completion within iterative learning control framework. Experimental results using multi-channel stimulation and 40 element surface electrode array confirm accurate tracking of selected hand postures. Based on the experimental results and the existing literature, a new system for the hand and wrist restoration has been designed. The key element of the system is a game-based task oriented training environment designed for a wide group of patients, including patients with spasticity and hemiplegia.

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More information

Published date: 15 January 2015
Organisations: University of Southampton, Electronics & Computer Science

Identifiers

Local EPrints ID: 388627
URI: https://eprints.soton.ac.uk/id/eprint/388627
PURE UUID: d98d4047-34e0-450c-9b87-98f8c9935b34

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Date deposited: 01 Mar 2016 11:44
Last modified: 17 Jul 2017 19:36

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Contributors

Author: Anna Soska
Thesis advisor: Eric Rogers
Thesis advisor: Christopher Freeman
Thesis advisor: Paul Lewin

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