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© 2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The recent advantage of new technologies coming hand-in-hand with Health 4.0 systems enables the acquisition of online handwriting signals, where temporal information is added to the x and y position. [...]by using a digitizing tablet, the analysis is not limited to spatial features which mainly quantify PD micrographia. [...]all of the extracted features were divided into nine different feature sets according to the type of the movement (on-surface, in-air, and combined) and the calculation approach, i.e., the type of feature (FD-based, conventional, and combined). [...]to evaluate and compare the power of the handwriting features’ ability to predict the values of the selected clinical characteristics (UPDRS V and PD duration), multivariate regression analysis was performed. [...]the FD-based features are better for modeling PD severity (in terms of UPDRS V score estimation), but they do not lead to an improvement in PD duration modeling.

Details

Title
Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting
Author
Mucha, Jan; Mekyska, Jiri; Galaz, Zoltan; Faundez-Zanuy, Marcos; Lopez-de-Ipina, Karmele; Zvoncak, Vojtech; Kiska, Tomas; Smekal, Zdenek; Brabenec, Lubos; Rektorova, Irena
Publication year
2018
Publication date
Dec 2018
Publisher
MDPI AG
e-ISSN
20763417
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2322064048
Copyright
© 2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.