Date of Award

2021

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Graduate Group

Bioengineering

First Advisor

Michelle J. Johnson

Abstract

Stroke and HIV are leading causes of disability worldwide. HIV is an independent risk factor for stroke, resulting in an emerging population dealing with both but without guidelines on how to manage the co-presentation of these conditions. There is a need for solutions to combat functional decline that results from the cognitive and motor dysfunction associated with these conditions. Rehabilitation robotics has been explored as a solution to provide therapy in the stroke population, but its application to people living with HIV has not yet been examined. Additionally, current technology-based approaches generally tend to treat cognitive and motor impairments in isolation. As such, a major barrier to the clinical utility of these approaches is that improvements on robotic rehabilitation tasks do not transfer to activities of daily living. In this thesis, I combine rehabilitation robotics, cognitive neuroscience, and bioengineering principles to design robot-based assessment tasks capable of measuring both cognitive and motor impairment. I use clinical assessment and robotic tools to first explore the impact of cognitive impairment on motor performance in the chronic stroke population. The results from this investigation demonstrate that motor performance on a robotic task is sensitive to cognitive impairment due to stroke. I then tested additional assessment tasks against standard clinical assessments of cognitive and motor function relevant in both HIV and stroke. These results showed the ability of robot-based metrics to capture differences in performance between varying levels of impairment among people living with HIV. After demonstrating the concurrent validity of this approach in the U.S., I implemented this approach in Botswana. The preliminary results demonstrated that robotic assessment was feasible in this context and that some of our models had good predictive value. This work expands the application of rehabilitation robotics to new populations, including people living with HIV, those with cognitive impairments, and people residing in LMICs. My hope is that the work presented in this thesis will lead to future efforts that can overcome the barriers to better health by enabling the development of more effective and accessible rehabilitation technologies.

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