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Computational Intelligence and Neuroscience
Volume 2013, Article ID 717853, 13 pages
Research Article

Finger Tapping Clinimetric Score Prediction in Parkinson's Disease Using Low-Cost Accelerometers

1Movere Group, Cyclotron Research Centre, University of Liege, Sart Tilman B30, 4000 Liège, Belgium
2Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Place du Levant 2, 1348 Louvain-la-Neuve, Belgium
3Institut de Recherche Exprimentale et Clinique, Center for Applied Molecular Technologies, Université Catholique de Louvain, Chapelle-aux-Champs 30, 1200 Woluwé-St-Lambert, Belgium
4Department of Neurology, University Hospital Centre, University of Liege, Sart Tilman B35, 4000 Liège, Belgium

Received 29 August 2012; Revised 4 March 2013; Accepted 5 March 2013

Academic Editor: Christian W. Dawson

Copyright © 2013 Julien Stamatakis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The motor clinical hallmarks of Parkinson's disease (PD) are usually quantified by physicians using validated clinimetric scales such as the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). However, clinical ratings are prone to subjectivity and inter-rater variability. The PD medical community is therefore looking for a simple, inexpensive, and objective rating method. As a first step towards this goal, a triaxial accelerometer-based system was used in a sample of 36 PD patients and 10 age-matched controls as they performed the MDS-UPDRS finger tapping (FT) task. First, raw signals were epoched to isolate the successive single FT movements. Next, eighteen FT task movement features were extracted, depicting MDS-UPDRS features and accelerometer specific features. An ordinal logistic regression model and a greedy backward algorithm were used to identify the most relevant features in the prediction of MDS-UPDRS FT scores, given by 3 specialists in movement disorders (SMDs). The Goodman-Kruskal Gamma index obtained (0.961), depicting the predictive performance of the model, is similar to those obtained between the individual scores given by the SMD (0.870 to 0.970). The automatic prediction of MDS-UPDRS scores using the proposed system may be valuable in clinical trials designed to evaluate and modify motor disability in PD patients.