Disaster detection by group learning using SVDD for Emergency Rescue Evacuation Support System

Abstract

A lot of people have got injured and died by sudden disasters such as fires and terrorisms. We have proposed an Emergency Rescue Support System (ERESS) for the purpose of reducing victims at the time of disaster. This system operates under mobile ad-hoc networks (MANET). So ERESS uses handheld terminals (ERESS terminals) such as smartphones and tablets. The ERESS terminals have disaster detection algorithm and plural sensors (acceleration, angular velocity, and geomagnetism). The sensors are used for the behavior analysis of ERESS terminal holders. By using the results of the analysis, the system detects the disaster from the behavior of people. In this paper, we propose a new disaster detection method by performing the machine learning in the group using a support vector domain description (SVDD). We are able to detect the behavior that is different from normal state of people in the disaster by using this method. The results of the performance evaluation by disaster simulation experiments show the validity of the proposed method.

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Cite this paper

@article{Komaki2015DisasterDB, title={Disaster detection by group learning using SVDD for Emergency Rescue Evacuation Support System}, author={Ken Komaki and Hiroko Higuchi and Haruka Iwahashi and Tomohiro Kitamura and Toshiki Yamasaki and Tomotaka Wada and Kazuhiro Ohtsuki}, journal={2015 International Telecommunication Networks and Applications Conference (ITNAC)}, year={2015}, pages={173-178} }