Identification of electrically stimulated muscle after stroke

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 trial-dependent and adaptive control schemes has been designed and implemented in the thesis

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