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Information 2018, 9(10), 247;

Dynamic Handwriting Analysis for Supporting Earlier Parkinson’s Disease Diagnosis

Computer Science Department, University of Bari, 70125 Bari, Italy
Author to whom correspondence should be addressed.
Received: 15 September 2018 / Revised: 25 September 2018 / Accepted: 28 September 2018 / Published: 3 October 2018
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
Full-Text   |   PDF [283 KB, uploaded 14 October 2018]


Machine learning techniques are tailored to build intelligent systems to support clinicians at the point of care. In particular, they can complement standard clinical evaluations for the assessment of early signs and manifestations of Parkinson’s disease (PD). Patients suffering from PD typically exhibit impairments of previously learned motor skills, such as handwriting. Therefore, handwriting can be considered a powerful marker to develop automatized diagnostic tools. In this paper, we investigated if and to which extent dynamic features of the handwriting process can support PD diagnosis at earlier stages. To this end, a subset of the publicly available PaHaW dataset has been used, including those patients showing only early to mild degree of disease severity. We developed a classification framework based on different classifiers and an ensemble scheme. Some encouraging results have been obtained; in particular, good specificity performances have been observed. This indicates that a handwriting-based decision support tool could be used to administer screening tests useful for ruling in disease. View Full-Text
Keywords: Parkinson’s disease; e-health; computer aided diagnosis; artificial intelligence; dynamic handwriting analysis Parkinson’s disease; e-health; computer aided diagnosis; artificial intelligence; dynamic handwriting analysis
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 (CC BY 4.0).

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Impedovo, D.; Pirlo, G.; Vessio, G. Dynamic Handwriting Analysis for Supporting Earlier Parkinson’s Disease Diagnosis. Information 2018, 9, 247.

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