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

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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