Parkinson's Disease monitoring using behavioural inferences and smartphones

A report submitted to the University of Manchester for the continuation to the second year of the doctor of philosophy degree program in the faculty of engineering and physical sciences.

Parkinson's Disease (PD) affects patients' motor and non-motor functionality. Traditional assessment techniques are inaccurate because PD symptoms vary throughout the day and are evaluated in sporadic and subjective sessions. Although recent works have benefited from electronic devices to try to overcome these issues, most are unsuitable to follow patients regularly for a long time. In contrast, my approach aims to monitor PD continuously in a longitudinal, naturalistic, non-disruptive and non-intrusive way. It uses smartphones to collect personal, social, environmental, and interaction data about patients and their surroundings. This data is processed to infer a set of metrics (a proxy) of people's activities and habits. Then, the proxy's trends are measured and mapped to the progression of the disease. This project aims to get a more accurate PD picture and to reduce the physical and psychological burden of traditional and alternative assessment methods. Thus, saving patients time and making health systems more financially efficient.