  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.
