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

TL;DR

This paper presents a deep learning approach using wrist-worn sensors to monitor Parkinson's disease motor states in daily life, aiming for personalized treatment adjustments.

Contribution

It introduces a novel neural network architecture, a post-training scheme, and a custom loss function for nine-level PD motor state estimation in free-living environments.

Findings

Achieved improved accuracy over previous models.

Established a new benchmark for nine-level PD motor state estimation.

Demonstrated feasibility of continuous monitoring with wearable devices.

Abstract

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.