Using Technology to Identify Children With Autism Through Motor Abnormalities - Simeoli, Roberta; Milano, Nicola; Rega, Angelo; Marocco, Davide (Frontiers in Psychology, 2021)
Autism is a neurodevelopmental disorder typically assessed and diagnosed through observational analysis of behavior. Assessment exclusively based on behavioral observation sessions requires a lot of time for the diagnosis. In the recent years it is growing the need to make assessment processes more motivating and capable to provide objective measures of the disorder. New evidence showed that motor abnormalities may underpin the disorder and provide a computational marker to enhance assessment and diagnostic processes. Thus, a measure of motor patterns could provide a means to assess young children with autism and a new starting point for rehabilitation treatments. In this study, we propose to use a software tool that through a smart tablet device and touch screen sensor technologies could be able to capture detailed information about children’s motor patterns. We compared movement trajectories of autistic children and typically developing children, with the aim to identify autism motor signatures analyzing their coordinates of movements. We used a smart tablet device to record coordinates of dragging movements carried out by 60 children (30 autistic children and 30 typically developing children) during a cognitive task. Machine learning analysis of children’s motor patterns identified autism with a 93% of accuracy, demonstrating that autism can be computationally identified. A descriptive analysis revealed the differentiation characteristics between the movement trajectories of the two groups, confirming that motor abnormalities are a core feature of autism.