A multi-layer Gaussian process for motor symptom estimation in people with Parkinson's Disease

Muriel Lang, Franz M. J. Pfister, Jakob Frohner, Kian Abedinpour, Daniel Pichler, Urban Fietzek, Terry Taewoong Um, Dana Kulic, Satoshi Endo, Sandra Hirche

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)


The assessment of Parkinson's disease (PD) poses a significant challenge, as it is influenced by various factors that 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 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 that 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 toward continuous monitoring of PD in the home environment.

Original languageEnglish
Pages (from-to)3038-3049
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Issue number11
Publication statusPublished - 1 Nov 2019
Externally publishedYes


  • Ambient intelligence
  • Gaussian processes
  • medical information system
  • wearable sensors

Cite this