The assessment of Parkinson's disease (PD) poses a significant challenge as it is influenced by various factors which lead to a complex and fluctuating symptom manifestation. Thus, a frequent and objective PD assessment is highly valuable for effective health management of people with Parkinson's disease (PwP). Here, we propose a method for monitoring PwP by stochastically modeling the relationships between their wrist movements during unscripted daily activities and corresponding annotations about clinical displays of movement abnormalities. We approach the estimation of PD motor signs by independently modeling and hierarchically stacking Gaussian process models for three classes of commonly observed movement abnormalities in PwP including tremor, (non-tremulous) bradykinesia, and (non-tremulous) dyskinesia. We use clinically adopted severity measures as annotations for training the models, thus allowing our multi-layer Gaussian process prediction models to estimate not only their presence but also their severities. The experimental validation of our approach demonstrates strong agreement of the model predictions with these PD annotations. Our results show the proposed method produces promising results in objective monitoring of movement abnormalities of PD in the presence of arbitrary and unknown voluntary motions, and makes an important step towards continuous monitoring of PD in the home environment.
@article{arxiv.1808.10663,
title = {A Multi-layer Gaussian Process for Motor Symptom Estimation in People with Parkinson's Disease},
author = {Muriel Lang and Franz M. J. Pfister and Jakob Fröhner and Kian Abedinpour and Daniel Pichler and Urban Fietzek and Terry T. Um and Dana Kulić and Satoshi Endo and Sandra Hirche},
journal= {arXiv preprint arXiv:1808.10663},
year = {2018}
}