Robots that facilitate touch by children have special requirements in terms of safety and robustness, but little is known about how and when children actually use touch with robots. Tools and techniques are required to sense the variety of children's touch and to interpret the volumes of data generated. This explorative user study investigated children's patterns of touch during game play with a robot. We examined where the children touch the robot and their patterns of touch over time, using a raster-based visualisation of each child's time series of touches, recording patterns of touch across different games and children. We found that children readily engage with the robot, in particular spontaneously touching the robot's hands more than any other area. This user study and the tools developed may aid future designs of robots to autonomously detect when they have been touched.

An important challenge in Human-Robot Interaction (HRI) is the analysis and interpretation of large volumes of data to inform the design process within a complex, multi-faceted research space. In this study, we explore how data visualization techniques can contribute to HRI methodology, particularly in terms of linking qualitative and quantitative analysis methods. Specifically, we present a case study demonstrating the visualization of touch data to identify potential patterns of interaction with a social robot intended for deployment in preschool classrooms.