Identifying Features that Characterize Children's Free-Hand Sketches using Machine Learning

Abstract

From an early age, children begin developing critical motor skills, such as fine motor control, that contribute significantly to reading, writing, drawing, and more, all of which are important for communication and school readiness. Pediatricians can evaluate a child's motor skills using activities and questionnaires. Sometimes these involve adults drawing with their child, but it can be difficult to fully evaluate a child's drawings through a handful of sketches from limited direct assessments. We propose creating a sketching system that will collect free-form drawing data from parents and children that can then automatically differentiate a child's sketch from an adult's using only the pen strokes of their drawing. In this paper, we describe our study that collected sketches from 14 children aged 2 to 5 and 25 adults over 18. We contribute a machine learning classifier based on sketch recognition features from free-hand drawings capable of distinguishing children's sketches from those made by adults with an F-measure of 0.906. These results indicate the potential of creating sketch-based applications for assessing children's fine motor development.