Smartphone detection of collapsed buildings during earthquakes

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.

Use of Mobile Crowdsensing in Disaster Management: A Systematic Review, Challenges, and Open Issues

“…The majority of the studies (12 papers) propose a System Architecture : Frommberger and Schmid [ 29 ] present a system architecture for an integrated disaster alerting and reporting system composed of an Android app, web interface, and disaster management server named Mobile4D. Visuri et al [ 30 ] present a two-tier system for building collapse detection in earthquakes that uses residents’ smartphones as distributed sensors. Anagnostopoulos et al [ 31 ] propose a Four-Layer system integrating crowdsourcing, crowdsensing, and an LSTM (long short-term memory) inference model for municipality resource allocation.…”

“…Six studies address this problem. Visuri et al [ 30 ] propose a building collapse detection system that uses end-user smartphones as distributed sensors accompanied by a rule-based fall detection algorithm. It is an empirical study with an evaluation of a fall detection algorithm through lab simulations and a limited field test.…”