A Comparison of Bayesian Filter Based Approaches for Patient Localization during Emergency Response to Crisis

Chandra-Sekaran, Ashok-Kumar; Weisser, Pascal; Müller-Glaser, Klaus D.; Kunze, Christophe

doi:10.1109/sensorcomm.2009.104

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…Most existing indoor localization solutions for building emergency response operations, either proposed in the academia [15][16][17][18][19][20][21][22][23] or are available in the market [24,25], were developed in the absence of clear knowledge about the needs of first responders if an indoor localization solution existed. This problem is even more obvious with indoor localization solutions developed for general purposes using various technologies such as inertial navigation systems (INS) [26][27][28], assisted GPS (AGPS) [29][30][31], and infrared [32][33][34], as well as a couple of RF technologies including ultra wide band (UWB) [35][36][37], radio frequency identification (RFID) [38][39][40][41], wireless local area network (WLAN) [42][43][44][45], and wireless sensor networks (WSN) [46][47][48].…”

Section: Need Analysismentioning

confidence: 99%

Comparative assessment of an indoor localization framework for building emergency response

Becerik-Gerber

Soibelman

et al. 2015

Automation in Construction

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Building emergencies, especially structure fires, are threats to the safety of both building occupants and first responders. It is difficult and dangerous for first responders to perform search and rescue in an unfamiliar environment, sometimes leading to secondary casualties. One way to reduce such hazards is to provide first responders with timely access to accurate location information. To address this challenge, the authors have developed a radio frequency based indoor localization framework, for which novel algorithms were designed for two different situations: one where an existing sensing infrastructure exists in buildings and one where an adhoc sensing infrastructure must be deployed. This paper presents a comparative assessment of this framework under different situations and emergency scenarios, and between simulations and field tests. The paper first presents an assessment of the framework in field tests, showing that it achieves room-level accuracies above 82.8% and 84.6% and coordinate-level accuracies above 2.29 m and 2.07 m, under the two situations, respectively. Moreover, the framework demonstrates considerable robustness in the tests, retaining a room-level accuracy of 70% or higher when the majority of sensing infrastructure is damaged. This paper then synthesizes results from both simulations and field tests, and demonstrates how the framework can be adapted to different situations and scenarios while consistently yielding satisfactory localization performance.

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Section: Need Analysismentioning

confidence: 99%

Comparative assessment of an indoor localization framework for building emergency response

Becerik-Gerber

Soibelman

et al. 2015

Automation in Construction

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“…There are a few indoor localization solutions proposed in literature, but none has been widely adopted. Chandra-Sekaran et al (2009a;2009b) proposed a system to locate doctors and patients carrying radio nodes in outdoor/indoor emergencies. Monte Carlo and unscented Kalman filter techniques were used for location estimation.…”

Section: Literature Reviewmentioning

confidence: 99%

An Environment-Aware Sequence-Based Localization Algorithm for Supporting Building Emergency Response Operations

Becerik-Gerber

Krishnamachari

et al. 2013

Computing in Civil Engineering

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Building emergencies are big threats to the safety of building occupants and first responders. When emergencies occur, unfamiliar environments are difficult and dangerous for first responders to search and rescue, sometimes leading to secondary casualties. One way to reduce such hazards is to provide first responders with timely access to accurate location information. Despite its importance, access to the location information at emergency scenes is far from being automated and efficient. This paper identifies a set of requirements for indoor localization during emergency response operations through a nationwide survey, and proposes an environmentaware sequence-based localization algorithm that is free of signal path loss models or collection of prior data, and mitigates signal multipath effects. The algorithm enables efficient on-scene ad-hoc sensor network deployment and optimizes sensing space division by strategically selecting sensor node locations. Building information is integrated, in order to enable building-specific space divisions and to support contextbased visualization of localization results. Proposed algorithm is evaluated through a building-size simulation. Room-level accuracy of up to 87.3% was reported, and up to 15.0% of deployment effort was reduced compared with using randomly selected sensor locations. The algorithm also showed good computational speed, with negligible time required for refreshing location estimation results in simulation. INTRODUCTIONBuilding emergencies, including flooding, building collapses, terrorist attacks and especially structure fires, are big threats to the safety of building occupants and first responders. For example, public fire departments across the U.S. attended 484,500 fires in buildings in 2011, which caused 2,460 deaths and 15,635 injuries (Karter 2012). When emergencies occur, unfamiliar environments are difficult and dangerous for first responders to search and rescue, sometimes leading to secondary casualties. With the increasing number of complex buildings, and less live-fire training, first responders are twice as likely to die inside structures as they were 20 years ago, and the leading cause of these line-of-duty deaths is getting lost, being trapped or disoriented (Brouwer 2007). One way to reduce such hazards is to provide firefighters with timely access to accurate location information. It is also of critical importance for an incident commander to know the locations of deployed first responders in real time, so that decision-making process is faster and more informed.

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Iterative Maximum Likelihood Estimation Algorithm: Leveraging Building Information and Sensing Infrastructure for Localization during Emergencies

Becerik-Gerber

Soibelman

2015

J. Comput. Civ. Eng.

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A Comparison of Bayesian Filter Based Approaches for Patient Localization during Emergency Response to Crisis

Cited by 6 publications

References 4 publications

Comparative assessment of an indoor localization framework for building emergency response

Comparative assessment of an indoor localization framework for building emergency response

An Environment-Aware Sequence-Based Localization Algorithm for Supporting Building Emergency Response Operations

Iterative Maximum Likelihood Estimation Algorithm: Leveraging Building Information and Sensing Infrastructure for Localization during Emergencies

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