Learning Bayesian Networks
Detecting and modeling play behavior using sensor-embedded rock-climbing equipment
Proceedings of the 9th International Conference on Interaction Design and Children
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Ubiquitous sensing technology and statistical modeling technology aremaking it possible to conduct scientific research on our everyday lives. These technologies enable us to quantitatively observe and record everyday life phenomena and thus acquire reusable knowledge from the large-scale sensory data. This paper proposes a "Spatio-temporal Semantic (STS) Mapping System," which is a general framework for modeling human behavior in an everyday life environment. The STS mapping system consists of a wearable sensor for spatially and temporally measuring human behavior in an everyday setting together with Bayesian network modeling software to acquire and retarget the gathered knowledge on human behavior. We consider this STS mapping system from both the theoretical and practical viewpoints. The theoretical framework describes a behavioral model in terms of a random field or as a point process in spatial statistics. The practical aspect of this paper is concerned with a case study in which the proposed system is used to create a new type of playground equipment design that is safer for children, in order to demonstrate the practical effectiveness of the system. In this case study, we studied children's behavior using a wireless wearable location-electromyography sensor that was developed by the authors, and then a behavioral model was constructed from the measured data. The case study shows that everyday life science can be used to improve product designs by measuring and modeling the way it is used.