From Online Handwriting to Synthetic Images for Alzheimer's Disease Detection Using a Deep Transfer Learning Approach

Early diagnosis of neurodegenerative disorders, such as Alzheimer's Disease (AD), is very important to reduce their effects and to improve both quality and life expectancy of patients. In this context, it is generally agreed that handwriting is one of the first skills altered by the onset of AD. For this reason, the analysis of handwriting and the study of its alterations has become of great interest in order to formulate the diagnosis as soon as possible. A fundamental aspect for the use of these techniques is the definition of effective features, which allows the system to distinguish the natural alterations of handwriting due to age, from those caused by neurodegenerative disorders. Starting from these considerations, the aim of our study is to verify whether the combined use of both shape and dynamic features allows a decision support system to improve performance for AD diagnosis. To this purpose, starting from a database of on-line handwriting samples, we generated for each of them an off-line synthetic color image, where the color of each elementary trait encodes, in the three RGB channels, the dynamic information associated with that trait. To verify the role played by dynamic information, we also generated simple binary images, containing only shape information. Finally, we exploited the ability of Convolutional Neural Network (CNN) to automatically extract features on both color and binary images. The experimental results have confirmed that dynamic information allows a performance improvement with respect to the binary images.