Although children’s behavioral and mental problems are generally diagnosed in clinical settings, the prediction and awareness of children’s mental wellness in daily settings are getting increased attention. Toy blocks are both accessible in most children’s daily lives and provide physicality as a unique non-verbal channel to express their inner world. In this paper, we propose a toy block approach for predicting a range of behavior problems in young children (4-6 years old) measured by the Child Behavior Checklist (CBCL). We defined and classified a set of quantitative play actions from IMU-embedded toy blocks. Play data collected from 78 preschoolers revealed that specific play actions and patterns indicate total problems, internalizing problems, and aggressive behavior in children. The results align with our qualitative observations, and suggest the potential of predicting the clinical behavior problems of children based on short free-play sessions with sensor-embedded toy blocks.
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)