A Machine Learning Approach to Analyze the Effects of Alzheimer's Disease on Handwriting through Lognormal Features

Documentdetail

ID kaart

oai:arXiv.org:2405.16959

Onderwerp

Computer Science - Computer Vision...

Auteur

D'Alessandro, Tiziana Carmona-Duarte, Cristina De Stefano, Claudio Diaz, Moises Ferrer, Miguel A. Fontanella, Francesco

Categorie

Computer Science

Jaar

2024

vermelding datum

29-05-2024

Trefwoorden

disease model analysis features alzheimer handwriting

Metriek

Beschrijving

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