A home-based functional electrical stimulation system for upper-limb stroke rehabilitation

Abstract

Due to an increased population of stroke patients and subsequent demand on health providers, there is an urgent need for effective stroke rehabilitation technology that can be used in patients' own homes. Over recent years, systems employing functional electrical stimulation (FES) have shown the ability to provide effective therapy. However, there is currently no low-cost therapeutic system available which simultaneously supplies FES to muscles in the patient's shoulder, arm and wrist to provide co-ordinated functional movement. This restricts the effectiveness of treatment, and hence the ability to support activities of daily living.
In this thesis a home-based low cost rehabilitation system is developed which substantially extends the current state of art in terms of sensing and control methodologies. In particular, it embeds novel non-contact sensing approaches; the first use of an electrode array within a closed-loop model based control scheme; an interactive task display system; and an integrated learning-based controller for multiple muscles within the upper-limb (UL), which supports co-ordinated tasks. The thesis then focuses on compacting the prototype by upgrading the depth sensor and using embedded systems to transfer it to the home environment.
Currently available home-based systems employing FES for UL rehabilitation are first reviewed in terms of their underlying technology, operation, scope and clinical evidence. Motivated by this, a detailed examination of a prototype system is carried out that combines low cost non-contact sensors with closed-loop FES controllers. Then potential avenues to extend the technology are highlighted, with specific focus given to low-cost non-contact based sensors for the hand and wrist. Sensing approaches are then reviewed and evaluated in terms of their scope to support the intended system requirements. Electrode array hardware is developed in order to provide accurate movement capability. Biomechanical models of the combined stimulated arm and mechanical support are then formulated. Using these, model-based iterative learning control methodologies are then designed and implemented.
The system is evaluated with both unimpaired participants and stroke patients undergoing a course of treatment. Finally, a home-based prototype is developed which integrates and extends the aforementioned components. Results confirm the system's scope to provide more effective stroke rehabilitation. Based on the achieved results, courses of future work necessary to continue this development are outlined.

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

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