Date of Award

12-2016

Degree Name

MS in Biomedical Engineering

Department

Biomedical and General Engineering

Advisor

Lily Laiho

Abstract

Current assessments for evaluating the progression of Parkinson’s Disease are largely qualitative and based on small sets of data obtained from occasional doctor-patient interactions. There is a clinical need to improve the techniques used for mitigating common Parkinson’s Disease symptoms. Available data sets for researching the disease are minimal, hindering advancement toward understanding the underlying causes and effectiveness of treatment and therapies. Mobile devices present an opportunity to continuously monitor Parkinson’s Disease patients and collect important information regarding the severity of symptoms. The evolution of digital technology has opened doors for clinical research to extend beyond the clinic by incorporating complex sensors in commonly used devices. Leveraging these sensors to quantify characteristic Parkinson’s Disease symptoms may drastically improve patient care and the reliability of symptom assessment.

The goal of this project is to design and develop a system for measuring and analyzing the cardinal symptoms of Parkinson’s using mobile devices. An application for the iPhone and Apple Watch is developed, utilizing the sensors on the devices to collect data during the performance of motor tasks. Assessments for tremor, bradykinesia, and postural instability are implemented to mimic UPDRS evaluations normally performed by a neurologist. The application connects to a cloud-based server to transfer the collected data for remote access and analysis. Example MatLab analysis demonstrates potential approaches for extracting meaningful data to be used for monitoring the progression of Parkinson’s Disease and the effectiveness of treatment and therapies. High-level verification testing is performed to show general efficacy of the assessment tasks. The system design successfully lays the groundwork for a mobile device-based assessment tool to objectively measure Parkinson’s Disease symptoms

Available for download on Monday, December 23, 2019

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