Kinematic features of a simple and short movement task to predict autism diagnosis.

Abstract

Autism is a developmental condition primarily identified by social and communication deficits. However, over 70% of autistic individuals also show motor function deficits, which are evident even when simple stereotyped movements are performed. In this study, we have asked 24 autistic and 22 non-autistic adults to perform pointing movements between two markers 30 cm apart as quickly and as accurately as they can for 10 seconds. Motion tracking was employed to collect data and calculate kinematic features of the movement and aiming accuracy. At the group level, the results showed that autistic individuals performed pointing movements slower but more accurately compared to non-autistic individuals. At the individual level, we have used Machine Learning methods to predict autism diagnosis. Nested result Cross-Validation was used, which in contrast to commonly used K-fold Cross-Validation avoids pooling training and testing data and provides robust performance estimates. Our developed models achieved a statistically significant classification accuracy of 71% and showed that even a simple and short motor task enables discrimination between autistic and non-autistic individuals.