Smartphone detection of collapsed buildings during earthquakes

Visuri, Aku; Zhu, Zeyun; Ferreira, Denzil; Konomi, Shin’ichi; Kostakos, Vassilis (2017-09-11)
 
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https://doi.org/10.1145/3123024.3124402

Visuri, Aku
Zhu, Zeyun
Ferreira, Denzil
Konomi, Shin’ichi
Kostakos, Vassilis
Association for Computing Machinery
11.09.2017

Aku Visuri, Zeyun Zhu, Denzil Ferreira, Shin’ichi Konomi, and Vassilis Kostakos. 2017. Smartphone detection of collapsed buildings during earthquakes. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (UbiComp '17). ACM, New York, NY, USA, 557-562. DOI: https://doi.org/10.1145/3123024.3124402

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© 2017 Copyright is held by the owner/author(s). | ACM 2017. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in UbiComp '17. Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. Maui, Hawaii Sept 11-15, 2017, https://doi.org/10.1145/3123024.3124402.
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doi:https://doi.org/10.1145/3123024.3124402
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Abstract

The leading cause of death during earthquakes is the collapse of urban infrastructures and the subsequent delay of emergency responders in identifying and reaching the affected sites. To overcome this challenge, we designed and evaluated a crowdsensing system that detects collapsed buildings using end-user smartphones as distributed sensors. We present our evaluation of smartphones’ accuracy in inferring a building collapse by detecting falls onto solid surfaces, and estimating the false positive rate. Further sensors can present more detailed information about each potential collapse event. We conduct simulations to identify strategies for dealing with false-positive data under scenarios of varying population density. Potential building collapses can be determined with 95% accuracy given 10 connected devices within a 125m radius, increasing to 99.99% for 50 devices. End-user devices can proactively offer valuable help to emergency responders during earthquakes, potentially saving lives.

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