Development of an upper limb rehabilitation system using functional electrical stimulation mediated by iterative learning control

Abstract

Stroke affects more than 150,000 people every year and is the third major cause of adult disability in the UK. Stroke rehabilitation plays an important part in the motor skills recovery of the stroke patients. This thesis forms part of the development of an upper arm rehabilitation system which involves the use of Functional Electrical Stimulation (FES). Motivation for this use of stimulation to augment remaining voluntary effort in strokepatients is explained and the necessary components comprising the system are described.

The task considered in this thesis is reaching, which involves elbow extension and shoulder elevation. FES is applied to two muscles, triceps and anterior deltoid respectively, to assist in these movements. A review of the literature has revealed possible control schemes which could be implemented with FES. Relatively few, however, have actually been implemented in clinical trials. This work, aims to apply selected controllers in clinical applications. A series of controllers are examined, starting from the simplest feedback controller going to more advanced model-based Iterative Learning Control (ILC) controllers. These include phase-lead ILC, input-output linearisation, and Newton-method based ILC. ILC algorithms are commonly used in industrial robots for precise control. The aim of this work is to transfer these algorithms to clinical settings.

ILC algorithms are used to provide finely-controlled levels of FES assistance to patients during repetitive training tasks. To use a model-based controller, kinematic and dynamic models of the Armeo and human arm have been developed. The muscle model of the human arm has been derived using a Hill-type model while the Hammerstein model is used to model the stimulated muscle. The complete system has then been used in a clinical study involving five stroke patients. Improvements in clinical measured Fugl-Meyer Assessment (FMA) scores were seen in the stroke patients after the trials.

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Identifiers

ORCID for Chris Freeman: orcid.org/0000-0003-0305-9246

ORCID for Eric Rogers: orcid.org/0000-0003-0179-9398

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