Data-driven prediction system for an environmental smartification approach to child fall accident prevention in a daily living space

Abstract

Ten thousand children are admitted to emergency rooms due to accidents every year in Tokyo. The most frequent accident is a fall accident. Fall accidents may occur when climbing to a high place in a daily living space. Since injury prevention by human supervision does not work well, the World Health Organization recommends an environmental modification approach as an effective preventive countermeasure to this problem. However, even for advanced human modeling technology, predicting where children can climb in everyday life situations remains difficult. In the present study, the authors developed a new method for predicting places that children can climb in a data-driven manner by integrating RGB-D cameras (Microsoft Kinect), a behavior recognition system (OpenPose), and a climbing motion planning algorithm based on a rapidly exploring random tree. The present paper describes fundamental functions of the developed system and presents an evaluation of the feasibility of the prediction function.