Reference : Finger Tapping feature extraction in Parkinson's disease using low-cost accelerometers
Scientific congresses and symposiums : Paper published in a journal
Engineering, computing & technology : Computer science
Human health sciences : Neurology
http://hdl.handle.net/2268/90129
Finger Tapping feature extraction in Parkinson's disease using low-cost accelerometers
English
Stamatakis, Julien [> > > >]
Cremers, Julien mailto [Université de Liège - ULg > Département des sciences cliniques > Neurologie]
Macq, Benoït [> > > >]
Garraux, Gaëtan mailto [Université de Liège - ULg > Département des sciences cliniques > Neurologie >]
2010
Proceedings 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB 2010)
Yes
Emerging Technologies for Patient Specific Healthcare. 2010 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB 2010)
Corfu
Greece
[en] parkinson's disease ; accelerometers
[en] The clinical hallmarks of Parkinson's disease (PD) are movement poverty and slowness (i.e. bradykinesia), muscle rigidity and limb tremor. The physicians usually quantify these motor disturbances by assigning a severity score according to validated but time-consuming clinical scales such as the Unified Parkinson's Disease Rating Scale (UPDRS) - part III. These clinical ratings are however prone to subjectivity and inter-rater variability. The PD medical community is therefore looking for a faster and more objective rating method. As a first step towards this goal, a tri-axial accelerometer-based system is proposed as patients are engaged in a repetitive finger tapping task, which is classically used to assess bradykinesia in the UPDRS-III. After developing the hardware, an algorithm has been developed, that automatically epoched the signal on a trial-by-trial basis and quantified, among others, movement speed, amplitude, hesitations or halts as validated by visual inspection of video recordings during the task. The results obtained in a PD patient and an healthy volunteer are presented. Preliminary results show that PD patients and healthy volunteers have different features profiles, so that a classifier could be set up to predict objective UPDRS-III scores.
Centre de Recherches du Cyclotron - CRC
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS ; Fonds pour la formation à la Recherche dans l'Industrie et dans l'Agriculture (Communauté française de Belgique) - FRIA
Researchers ; Professionals
http://hdl.handle.net/2268/90129
10.1109/ITAB.2010.5687769

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