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Handwriting Analysis to Support Alzheimer’s Disease Diagnosis: A Preliminary Study

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11679)

Abstract

Alzheimer’s disease (AD) is the most common neurodegenerative dementia of old age and the leading chronic disease contributor to disability and dependence among older people worldwide. Handwriting is among the motor activities compromised by AD, which is the result of a complex network of cognitive, kinaesthetic and perceptive-motor skills. Indeed, researchers have shown that the patients affected by these diseases exhibit alterations in the spatial organization and poor control of movement. In this paper, we present the preliminary results of a study in which an experimental protocol (including the copy of words, letters and sentence task) has been used to assess the kinematic properties of the movements involved in the handwriting. The obtained results are very encouraging and seem to confirm the hypothesis that machine learning-based analysis of handwriting can be profitably used to support AD diagnosis.

Keywords

Handwriting Classification algorithm Alzheimer’s disease 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electrical and Information Engineering (DIEI)University of Cassino and Southern LazioCassinoItaly

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