Granular Motor State Monitoring of Free Living Parkinson's Disease Patients via Deep Learning
This work addresses the need for personalized medication schedules for Parkinson's disease patients by providing objective, precise monitoring, though it builds incrementally on previous research.
The paper tackles the problem of continuously monitoring motor symptoms in Parkinson's disease patients during daily activities by using a wrist-worn smartwatch with 3D motion sensors, achieving a novel benchmark for nine-level motor state estimation.
Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide and affects around 1% of the (60+ years old) elderly population in industrial nations. More than 80% of PD patients suffer from motor symptoms, which could be well addressed if a personalized medication schedule and dosage could be administered to them. However, such personalized medication schedule requires a continuous, objective and precise measurement of motor symptoms experienced by the patients during their regular daily activities. In this work, we propose the use of a wrist-worn smart-watch, which is equipped with 3D motion sensors, for estimating the motor fluctuation severity of PD patients in a free-living environment. We introduce a novel network architecture, a post-training scheme and a custom loss function that accounts for label noise to improve the results of our previous work in this domain and to establish a novel benchmark for nine-level PD motor state estimation.