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

TL;DR

This paper introduces a machine learning method utilizing lognormal features derived from the sigma-lognormal model to analyze handwriting for potential early detection and understanding of Alzheimer's disease.

Contribution

It presents a novel application of lognormal features from handwriting analysis combined with machine learning for Alzheimer's assessment.

Findings

Effective feature extraction from handwriting data

Potential for early Alzheimer's detection

Insights into handwriting changes associated with Alzheimer's

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.