Unobtrusive and Personalised Monitoring of Parkinson's Disease Using Smartphones

Abstract

Parkinson's Disease is a neurodegenerative condition with no cure and a wide variety of idiosyncratic motor and non-motor symptoms that impact people's quality of life. In infrequent and short clinical consultations, health care professionals use validated clinical scales that are time-consuming and rely on clinical expertise or patients' perception. Moreover, symptoms can fluctuate within or between days due to the natural progression of the disease or medication and thus, a short session only provides a snapshot and not a longitudinal image of the disease. In practice, it is difficult for clinicians to tailor treatments and medication to each person's symptoms. Therefore, a granular, continuous, and objective methodology to track symptom fluctuations would improve patients' quality of life and make health services more efficient. The complexity of Parkinson's makes it a challenging but suitable study case to investigate new personalised approaches supported by technology and tailored to each patient's condition. This work aims to explore the use of behavioural inferences extracted from smartphone data to track the fluctuations in Parkinson's Disease symptoms. We recruited 7 participants with early Parkinson's (Hoehn & Yahr scale

Date of Award	1 Aug 2019
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Caroline Jay (Supervisor), Simon Harper (Supervisor) & Markel Vigo (Supervisor)