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Granular Motor State Monitoring of Free Living Parkinson's Disease Patients via Deep Learning

by   Kamer A. Yuksel, et al.
Universität München

Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide and affects around 1 industrial nations. More than 80 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.


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