Feasibility of Game-Based Tablet Motor Assessments for Autism Severity Classification

Motor difficulties are common in children with Autism Spectrum Disorder (ASD), particularly in tasks that require both cognitive and motor control, such as manual dexterity, eye–hand coordination, and visual–motor integration, and these challenges are often associated with the severity of symptoms. This pilot study aimed to investigate the feasibility of classifying ASD severity by evaluating learning abilities through motor execution data collected from tablet-based dragging tasks. Sixteen children with ASD participated in a 4-week game-based intervention, during which they performed daily routine tasks in a digital game. Gameplay involved selecting and dragging objects to complete task steps. Three distinct dragging styles — straight, curved, and zigzag — were extracted to assess learning performance. Features such as trajectory accuracy, curviness, duration, and number of attempts were analyzed, and machine-learning classifiers were applied to differentiate between mild, moderate, and severe ASD. The zigzag task demonstrated the strongest potential (81.6% F1-score in mild vs. moderate severity), indicating that task complexity is crucial for revealing motor differences. Although preliminary and with limited effectiveness for simpler tasks, these findings establish a proof of concept for tablet-based assessments supporting individualized ASD monitoring. Future work will expand the dataset and refine models to enhance their clinical utility and personalize therapeutic interventions.