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Sensors (Basel). 2016 Jun 21;16(6). pii: E930. doi: 10.3390/s16060930.

Analysis and Visualization of 3D Motion Data for UPDRS Rating of Patients with Parkinson's Disease.

Author information

1
Institute of Medical Engineering and Mechatronics, Ulm University of Applied Sciences, Albert-Einstein-Allee 55, Ulm D-89081, Germany. piro@hs-ulm.de.
2
Faculty of Physics, Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, Munich D-80539, Germany. lennart.piro@campus.lmu.de.
3
Department of Neurology, University of Ulm, Oberer Eselsberg 45, Ulm D-89081, Germany. jan.kassubek@uni-ulm.de.
4
Institute of Medical Engineering and Mechatronics, Ulm University of Applied Sciences, Albert-Einstein-Allee 55, Ulm D-89081, Germany. blechschmidt-trapp@hs-ulm.de.

Abstract

Remote monitoring of Parkinson's Disease (PD) patients with inertia sensors is a relevant method for a better assessment of symptoms. We present a new approach for symptom quantification based on motion data: the automatic Unified Parkinson Disease Rating Scale (UPDRS) classification in combination with an animated 3D avatar giving the neurologist the impression of having the patient live in front of him. In this study we compared the UPDRS ratings of the pronation-supination task derived from: (a) an examination based on video recordings as a clinical reference; (b) an automatically classified UPDRS; and (c) a UPDRS rating from the assessment of the animated 3D avatar. Data were recorded using Magnetic, Angular Rate, Gravity (MARG) sensors with 15 subjects performing a pronation-supination movement of the hand. After preprocessing, the data were classified with a J48 classifier and animated as a 3D avatar. Video recording of the movements, as well as the 3D avatar, were examined by movement disorder specialists and rated by UPDRS. The mean agreement between the ratings based on video and (b) the automatically classified UPDRS is 0.48 and with (c) the 3D avatar it is 0.47. The 3D avatar is similarly suitable for assessing the UPDRS as video recordings for the examined task and will be further developed by the research team.

KEYWORDS:

IMU; MARG sensors; Parkinson’s Disease; UPDRS; animated 3D avatar; diadochokinesis; inertia sensors; motion data; pronation-supination; remote monitoring; symptom quantification; telemonitoring

PMID:
27338400
PMCID:
PMC4934355
DOI:
10.3390/s16060930
[Indexed for MEDLINE]
Free PMC Article

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