Détail du document
Identifiant

oai:arXiv.org:2405.16959

Sujet
Computer Science - Computer Vision...
Auteur
D'Alessandro, Tiziana Carmona-Duarte, Cristina De Stefano, Claudio Diaz, Moises Ferrer, Miguel A. Fontanella, Francesco
Catégorie

Computer Science

Année

2024

Date de référencement

29/05/2024

Mots clés
disease model analysis features alzheimer handwriting
Métrique

Résumé

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.

D'Alessandro, Tiziana,Carmona-Duarte, Cristina,De Stefano, Claudio,Diaz, Moises,Ferrer, Miguel A.,Fontanella, Francesco, 2024, A Machine Learning Approach to Analyze the Effects of Alzheimer's Disease on Handwriting through Lognormal Features

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