Using handwriting features to characterize cognitive impairment

Abstract

Cognitive impairments affect skills such as communication, understanding or memory and they may be a short-term problem or a permanent condition. Among the diseases involving cognitive impairments, neurodegenerative ones are the most common and affect millions of people worldwide. Handwriting is one of the daily activities affected by these kinds of impairments, and its anomalies are already used as diagnosis sign, e.g. micrographia in Parkinson’s patients. Nowadays, many studies have been conducted to investigate how cognitive impairments affect handwriting, but few of them have used classification algorithms as a tool to support the diagnosis of these diseases. Moreover, almost all of these studies have involved a few dozens of subjects. In this paper, we present a study in which the handwriting of more than one hundred subjects has been recorded while they were performing some elementary tasks, such as the copy of simple words or the drawing of elementary forms. As for the features, we used those related to the handwriting movements. The results seem to confirm that handwriting analysis can be used to develop machine learning tools to support the diagnosis of cognitive impairments.