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.
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Acknowledgements
This work has been supported by the Spanish project PID2021-122687OA-I00/AEI/10.13039/501100011033/FEDER, UE.
The research leading to these results has received funding from Project “Ecosistema dell’innovazione - Rome Technopole” financed by EU in NextGenerationEU plan through MUR Decree n. 1051 23.06.2022 - CUP H33C22000420001.
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D’Alessandro, T., Carmona-Duarte, C., De Stefano, C., Diaz, M., Ferrer, M.A., Fontanella, F. (2023). A Machine Learning Approach to Analyze the Effects of Alzheimer’s Disease on Handwriting Through Lognormal Features. In: Parziale, A., Diaz, M., Melo, F. (eds) Graphonomics in Human Body Movement. Bridging Research and Practice from Motor Control to Handwriting Analysis and Recognition. IGS 2023. Lecture Notes in Computer Science, vol 14285. Springer, Cham. https://doi.org/10.1007/978-3-031-45461-5_8
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