Xiyue Wang

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.

Natural disasters cause long-lasting mental health problems such as PTSD in children. Following the 2011 Earthquake and Tsunami in Japan, we witnessed a shift of toy block play behavior in young children who suffered from stress after the disaster. The behavior reflected their emotional responses to the traumatic event. In this paper, we explore the feasibility of using data captured from block-play to assess children’s stress after a major natural disaster. We prototyped sets of sensor-embedded toy blocks, AssessBlocks, that automate quantitative play data acquisition. During a three-year period, the blocks were dispatched to fifty-two post-disaster children. Within a free play session, we captured block features, a child’s playing behavior, and stress evaluated by several methods. The result from our analysis reveal correlations between block play features and stress measurements and show initial promise of using the effectiveness of using AssessBlocks to assess children’s stress after a disaster. We provide detailed insights into the potential as well as the challenges of our approach and unique conditions. From these insights we summarize guidelines for future research in automated play assessment systems that support children’s mental health.