Characterizing Children's Motion Qualities: Implications for the Design of Motion Applications for Children

The goal of this paper is to understand differences between children's and adults’ motions in order to improve future motion recognition algorithms for children. Motion-based applications are becoming increasingly popular among children (e.g., games). These applications often rely on accurate recognition of users’ motions to create meaningful interactive experiences. Motion recognition systems are usually trained on adults’ motions. However, prior work has shown that children move differently from adults. Therefore, these systems will likely perform poorly on children's motions, negatively impacting their interactive experiences. Although prior work has established that there are perceivable differences between child and adult motion, these differences are yet to be quantified. If we can quantify these differences, then we can gain new insights about how children perform motions (i.e., their motion qualities). We present 24 articulation features (11 of which we newly developed) that describe motions quantitatively; we then evaluate them on a subset of child and adult motions from the publicly available Kinder-Gator dataset to reveal differences; motions in this dataset are represented as postures, each of which is defined by 3D positions of 20 joints tracked by a Kinect at a specific time instance. Our results showed that children perform motions that are quantifiably faster, more intense, less smooth, and less coordinated as compared to adults. Based on our results, we propose guidelines for improving motion recognition algorithms and designing motion applications for children.