Performance-Driven Handwriting Task Selection for Parkinson's Disease Classification

Diagnosing and monitoring Parkinson’s disease (PD) is a topic of current research in many fields, including AI. The innovative challenge is to develop a low-cost, non-invasive tool to support clinicians at the point of care. In particular, since handwriting difficulties in PD patients are well-known, changes in handwriting have emerged as a powerful discriminant factor for PD assessment. A crucial step in designing a decision support system based on handwriting concerns the choice of the most appropriate handwriting tasks to be administered for data acquisition. When data are collected, traditional approaches assume that different tasks, although not with the same impact, are all important for classification. However, not all tasks are likely to be useful for diagnosis, and the inclusion of these tasks may be detrimental to prediction accuracy. This work investigates the potential of an optimal subset of tasks for a more accurate PD classification. The evaluation is carried out by adopting a performance-driven multi-expert approach on different handwriting tasks performed by the same subjects. The multi-expert system is based on similar or conceptually different classifiers trained on features related to the dynamics of the handwriting process. The proposed approach improves baseline results on the PaHaW data set.