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The assessment of Parkinson's disease (PD) poses a significant challenge asit is influenced by various factors which lead to a complex and fluctuatingsymptom manifestation. Thus, a frequent and objective PD assessment is highlyvaluable for effective health management of people with Parkinson's disease(PwP). Here, we propose a method for monitoring PwP by stochastically modelingthe relationships between their wrist movements during unscripted dailyactivities and corresponding annotations about clinical displays of movementabnormalities. We approach the estimation of PD motor signs by independentlymodeling and hierarchically stacking Gaussian process models for three classesof commonly observed movement abnormalities in PwP including tremor,(non-tremulous) bradykinesia, and (non-tremulous) dyskinesia. We use clinicallyadopted severity measures as annotations for training the models, thus allowingour multi-layer Gaussian process prediction models to estimate not only theirpresence but also their severities. The experimental validation of our approachdemonstrates strong agreement of the model predictions with these PDannotations. Our results show the proposed method produces promising results inobjective monitoring of movement abnormalities of PD in the presence ofarbitrary and unknown voluntary motions, and makes an important step towardscontinuous monitoring of PD in the home environment.

A Multi-layer Gaussian Process for Motor Symptom Estimation in People with Parkinson's Disease