This research paper investigates how word semantics and phonology affect the handwriting of Alzheimer’s disease patients. The study uses machine learning techniques to analyze data from six handwriting tasks, each requiring the copying of a word from different categories: regular, non-regular, and non-word. The results show that different word types yield distinctive feature sets, with non-regular words requiring more features but achieving excellent classification performance.
Publication date: July 11, 2023
Project Page: N/A
Paper: https://arxiv.org/pdf/2307.04762
