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.
| Original language | English |
|---|---|
| Title of host publication | UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 557-562 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781450351904 |
| DOIs | |
| Publication status | Published - Sep 11 2017 |
| Event | 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers, UbiComp/ISWC 2017 - Maui, United States Duration: Sep 11 2017 → Sep 15 2017 |
Publication series
| Name | UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers |
|---|
Conference
| Conference | 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers, UbiComp/ISWC 2017 |
|---|---|
| Country | United States |
| City | Maui |
| Period | 9/11/17 → 9/15/17 |
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All Science Journal Classification (ASJC) codes
- Software
- Hardware and Architecture
- Computer Networks and Communications
Cite this
Smartphone detection of collapsed buildings during earthquakes. / Visuri, Aku; Zhu, Zeyun; Ferreira, Denzil; Konomi, Shin'ichi; Kostakos, Vassilis.
UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. Association for Computing Machinery, Inc, 2017. p. 557-562 (UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Smartphone detection of collapsed buildings during earthquakes
AU - Visuri, Aku
AU - Zhu, Zeyun
AU - Ferreira, Denzil
AU - Konomi, Shin'ichi
AU - Kostakos, Vassilis
PY - 2017/9/11
Y1 - 2017/9/11
N2 - 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.
AB - 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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UR - http://www.scopus.com/inward/citedby.url?scp=85030867540&partnerID=8YFLogxK
U2 - 10.1145/3123024.3124402
DO - 10.1145/3123024.3124402
M3 - Conference contribution
T3 - UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers
SP - 557
EP - 562
BT - UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers
PB - Association for Computing Machinery, Inc
ER -