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Proceedings Paper

Automatic characterization of the Parkinson disease by classifying the ipsilateral coordination and spatiotemporal gait patterns
Author(s): Fernanda Sarmiento; Fabio Martínez; Eduardo Romero
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Paper Abstract

Traditionally, the Parkinson disease is diagnosed and followed up by conventional clinical tests that are fully dependent on the expert experience. The diffuse boundary between normal and early Parkinson stages and the high variability of gait patterns difficult any objective characterization of this disease. An automatic characterization of the disease is herein proposed by mixing up different measures of the ipsilateral coordination and spatiotemporal gait patterns which are then classified with a classical support vector machine. The strategy was evaluated in a population with Parkinson and healthy control subjects, obtaining an average accuracy of 87% for the task of classification.

Paper Details

Date Published: 28 January 2015
PDF: 5 pages
Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 928719 (28 January 2015); doi: 10.1117/12.2073529
Show Author Affiliations
Fernanda Sarmiento, Univ. Nacional de Colombia (Colombia)
Fabio Martínez, Univ. Nacional de Colombia (Colombia)
Eduardo Romero, Univ. Nacional de Colombia (Colombia)


Published in SPIE Proceedings Vol. 9287:
10th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore, Editor(s)

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