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Title: A Machine Learning Approach to Analyze the Effects of Alzheimer’s Disease on Handwriting Through Lognormal Features
Authors: D’Alessandro, Tiziana
Carmona Duarte, María Cristina 
De Stefano, Claudio
Díaz Cabrera, Moisés 
Ferrer Ballester, Miguel Ángel 
Fontanella, Francesco
UNESCO Clasification: 3307 Tecnología electrónica
3314 Tecnología médica
Issue Date: 2023
Publisher: Springer 
Project: Modelo Computacional Del Aprendizajey la Degeneración Del Movimiento Humano Para Su Aplicación en Diagnóstico Clínico 
Conference: 21st Conference of the International Graphonomics Society (IGS2023) 
Abstract: Alzheimer’s disease is one of the most incisive illnesses among the neurodegenerative ones, and it causes a progressive decline in cognitive abilities that, in the worst cases, becomes severe enough to interfere with daily life. Currently, there is no cure, so an early diagnosis is strongly needed to try and slow its progression through medical treatments. Handwriting analysis is considered a potential tool for detecting and understanding certain neurological conditions, including Alzheimer’s disease. While handwriting analysis alone cannot provide a definitive diagnosis of Alzheimer’s, it may offer some insights and be used for a comprehensive assessment. The Sigma-lognormal model is conceived for movement analysis and can also be applied to handwriting. This model returns a set of lognormal parameters as output, which forms the basis for the computation of novel and significant features. This paper presents a machine learning approach applied to handwriting features extracted through the sigma-lognormal model. The aim is to develop a support system to help doctors in the diagnosis and study of Alzheimer, evaluate the effectiveness of the extracted features and finally study the relation among them.
ISBN: 978-3-031-45460-8
ISSN: 0302-9743
DOI: 10.1007/978-3-031-45461-5_8
Source: 21st Conference of the International Graphonomics Society (IGS2023). Lecture Notes in Computer Science 14285, p. 103-121
Appears in Collections:Actas de congresos
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