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

Tsubasa Nose, Koji Kitainura, Mikiko Oono, Micliiko Ohkura, Yoshifunii Nishida

研究成果: Conference article査読

2 被引用数 (Scopus)

抄録

Ten thousand children are admitted to emergency rooms due to accidents every year in Tokyo. Hie most frequent accident is a fall accident. Fall accidents may occur when clnnbmg 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 countenneasure to this problem However, even for advanced human modeling teclinology. predicting where children can clnnb in everyday life situations reinams difficult. In the present study, the authors developed a new method for predicting places that children can clnnb in a data-driven manner by mtegratmg RGB-D cameras (Microsoft Kinect), a behavior recognition system (OpenPose). and a climbmg motion plannmg 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.

本文言語English
ページ(範囲)126-133
ページ数8
ジャーナルProcedia Computer Science
160
DOI
出版ステータスPublished - 2019
外部発表はい
イベント10th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2019 and 9th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2019, Affiliated Workshops - Coimbra, Portugal
継続期間: 2019 11月 42019 11月 7

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)

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