Characterizing Early-Stage Alzheimer Through Spatiotemporal Dynamics of Handwriting

We propose an original approach for characterizing early Alzheimer, based on the analysis of online handwritten cursive loops. Unlike the literature, we model the loop velocity trajectory (full dynamics) in an unsupervised way. Through a temporal clustering based on K-medoids, with dynamic time warping as dissimilarity measure, we uncover clusters that give new insights on the problem. For classification, we consider a Bayesian formalism that aggregates the contributions of the clusters, by probabilistically combining the discriminative power of each. On a dataset consisting of two cognitive profiles, early-stage Alzheimer disease and healthy persons, each comprising 27 persons collected at Broca Hospital in Paris, our classification performance significantly outperforms the state-of-the-art, based on global kinematic features.