Passive Monitoring of Parkinson Tremor in Daily Life
A Prototypical Network Approach
Luc Evers (Radboud University Medical Center, Radboud Universiteit Nijmegen)
Yordan P. Raykov (University of Nottingham)
Tom M. Heskes (Radboud Universiteit Nijmegen)
Jesse Krijthe (TU Delft - Pattern Recognition and Bioinformatics)
Bastiaan R. Bloem (Radboud University Medical Center)
Max A. Little (University of Birmingham)
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Abstract
Objective and continuous monitoring of Parkinson’s disease (PD) tremor in free-living conditions could benefit both individual patient care and clinical trials, by overcoming the snapshot nature of clinical assessments. To enable robust detection of tremor in the context of limited amounts of labeled training data, we propose to use prototypical networks, which can embed domain expertise about the heterogeneous tremor and non-tremor sub-classes. We evaluated our approach using data from the Parkinson@Home Validation study, including 8 PD patients with tremor, 16 PD patients without tremor, and 24 age-matched controls. We used wrist accelerometer data and synchronous expert video annotations for the presence of tremor, captured during unscripted daily life activities in and around the participants’ own homes. Based on leave-one-subject-out cross-validation, we demonstrate the ability of prototypical networks to capture free-living tremor episodes. Specifically, we demonstrate that prototypical networks can be used to enforce robust performance across domain-informed sub-classes, including different tremor phenotypes and daily life activities.
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