Classifying the Kinematics of Fast Pen Strokes in Children with ADHD using Different Machine Learning Models

Published in The Lognormality Principle and Its Applications in e-Security, e-Learning and e-Health, 2021

Abstract: This exploratory study examines whether the sigma-lognormal model derived from the Kinematic Theory of rapid human movements discriminates between the handwriting strokes produced by children with and without Attention Deficit Hyperactivity Disorder (ADHD). Twelve children with ADHD and 12 controls aged 8–11 years were asked to produce handwriting strokes on a digitizing tablet. The strokes were analyzed using the sigma-lognormal model. Strokes made by children with ADHD reflected poorer motor control, action planning and execution than strokes made by controls. Different Machine learning models were trained to classify the subjects according to the discriminatory parameters used as features. Although the sample size and data are modest and will require replication in a larger forthcoming study, promising preliminary results are obtained, suggesting that the sigma-lognormal model may be a useful tool in the assessment of ADHD.

Recommended citation: Nadir Faci, Hung Nguyen, Patricia Laniel, Gauthier Bruno, Miriam Beauchamp, Masaki Nakagawa, Réjean Plamondon, "Classifying the Kinematics of Fast Pen Strokes in Children with ADHD using Different Machine Learning Models." The Lognormality Principle and Its Applications in e-Security, e-Learning and e-Health, 2021.