Monitoring Parkinson's Disease Progression Using Behavioural Inferences, Mobile Devices and Web Technologies

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 utilised wearable devices to try to overcome these issues, most are unsuitable for following 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 smart-phones to log and transmit over the Internet social, environmental, and interaction data about patients and their surroundings. This data is complemented with other web data sources (i.e., geographical and weather data) and then processed to infer a set of metrics (a latent behavioural variable or LBV) of people's activities and habits. Then, the LBV's trends are measured and mapped to the progression of the disease. As a part of the pilot study to test the proposed methodology, I have collected approximate to 290 million records from 2 patients, making this dataset 34.5x bigger and 4x richer than state-of-the-art sets. I used the collected data to identify six possible PD-related LBVs. This project aims to get a more accurate disease picture and to reduce the physical and psychological burden of traditional assessment methods. Ultimately, the work has the potential to save patients' time and improve the efficiency and effectiveness of health services.