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Victim Localization and Assessment System for Emergency Responders
In minor to moderate natural and man-made disasters, such as earthquakes and fires, people may be trapped inside buildings and hurt by the disaster. Considering that trapped victims may be unconscious, there is a high demand by emergency responders to get information on the locations and physical statuses of trapped victims inside a building during a disaster. In this paper, a smartphone-based, in-building emergency response assistance system, named iRescue, is presented. The system is comprised of two subsystems: a Victim Positioning System (VPS) and a Victim Assessment System (VAS). The VPS uses the received signal strength indicator of Wi-Fi signals from multiple wireless access points with referencing a pre-established Wi-Fi fingerprinting map of a building. The VAS uses patterns obtained from measured 3D acceleration changes by status of a victim. A Naïve Bayes classifier is employed for both VPS and VAS: for localization in between the fingerprinting map and for recognition of activities to be used for status assessment. The performance of the VPS has been validated by a localization test on a complex building. The VAS has been validated by activity simulation test with five people and real-time monitoring of a person equipped with an activity recording device.
Victim Localization and Assessment System for Emergency Responders
In minor to moderate natural and man-made disasters, such as earthquakes and fires, people may be trapped inside buildings and hurt by the disaster. Considering that trapped victims may be unconscious, there is a high demand by emergency responders to get information on the locations and physical statuses of trapped victims inside a building during a disaster. In this paper, a smartphone-based, in-building emergency response assistance system, named iRescue, is presented. The system is comprised of two subsystems: a Victim Positioning System (VPS) and a Victim Assessment System (VAS). The VPS uses the received signal strength indicator of Wi-Fi signals from multiple wireless access points with referencing a pre-established Wi-Fi fingerprinting map of a building. The VAS uses patterns obtained from measured 3D acceleration changes by status of a victim. A Naïve Bayes classifier is employed for both VPS and VAS: for localization in between the fingerprinting map and for recognition of activities to be used for status assessment. The performance of the VPS has been validated by a localization test on a complex building. The VAS has been validated by activity simulation test with five people and real-time monitoring of a person equipped with an activity recording device.
Victim Localization and Assessment System for Emergency Responders
Yoon, Hyungchul (Autor:in) / Shiftehfar, Reza (Autor:in) / Cho, Soojin (Autor:in) / Spencer, Billie F. (Autor:in) / Nelson, Mark E. (Autor:in) / Agha, Gul (Autor:in)
27.02.2015
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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