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An immersive virtual reality serious game to enhance earthquake behavioral responses and post-earthquake evacuation preparedness in buildings
Indexado
WoS WOS:000552715300016
Scopus SCOPUS_ID:85085733984
DOI 10.1016/J.AEI.2020.101118
Año 2020
Tipo artículo de investigación

Citas Totales

Autores Afiliación Chile

Instituciones Chile

% Participación
Internacional

Autores
Afiliación Extranjera

Instituciones
Extranjeras


Abstract



Enhancing the earthquake behavioral responses and post-earthquake evacuation preparedness of building occupants is beneficial to increasing their chances of survival and reducing casualties after the mainshock of an earthquake. Traditionally, training approaches such as seminars, posters, videos or drills are applied to enhance preparedness. However, they are not highly engaging and have limited sensory capabilities to mimic life-threatening scenarios for the purpose of training potential participants. Immersive Virtual Reality (IVR) and Serious Games (SG) as innovative digital technologies can be used to create training tools to overcome these limitations. In this study, we propose an IVR SG-based training system to improve earthquake behavioral responses and post-earthquake evacuation preparedness. Auckland City Hospital was chosen as a case study to test our IVR SG training system. A set of training objectives based on best evacuation practice has been identified and embedded into several training scenarios of the IVR SG. Hospital staff (healthcare and administrative professionals) and visitors were recruited as participants to be exposed to these training scenarios. Participants' preparedness has been measured along two dimensions: 1) Knowledge about best evacuation practice; 2) Self-efficacy in dealing with earthquake emergencies. Assessment results showed that there was a significant knowledge and self-efficacy increase after the training. In addition, participants acknowledged that it was easy, helpful, and engaging to learn best evacuation practice knowledge through the IVR SG training system.

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Disciplinas de Investigación



WOS
Engineering, Multidisciplinary
Computer Science, Artificial Intelligence
Scopus
Information Systems
Artificial Intelligence
SciELO
Sin Disciplinas

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Publicaciones WoS (Ediciones: ISSHP, ISTP, AHCI, SSCI, SCI), Scopus, SciELO Chile.

Colaboración Institucional



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Autores - Afiliación



Ord. Autor Género Institución - País
1 Feng, Zhenan - UNIV AUCKLAND - Nueva Zelanda
Massey Univ - Nueva Zelanda
University of Auckland - Nueva Zelanda
Massey University Manawatu - Nueva Zelanda
The University of Auckland - Nueva Zelanda
Massey University - Nueva Zelanda
2 Gonzalez, Vicente A. Hombre UNIV AUCKLAND - Nueva Zelanda
University of Auckland - Nueva Zelanda
The University of Auckland - Nueva Zelanda
3 Amor, Robert Hombre UNIV AUCKLAND - Nueva Zelanda
University of Auckland - Nueva Zelanda
The University of Auckland - Nueva Zelanda
4 Spearpoint, Michael Hombre OFR Consultants - Reino Unido
5 Thomas, Jared Hombre WSP Opus - Nueva Zelanda
6 Sacks, Rafael Hombre Technion Israel Inst Technol - Israel
Technion - Israel Institute of Technology - Israel
7 Lovreglio, Ruggiero Hombre Massey Univ - Nueva Zelanda
Massey University Manawatu - Nueva Zelanda
Massey University - Nueva Zelanda
8 CABRERA-GUERRERO, GUILLERMO NICOLAS Hombre Pontificia Universidad Católica de Valparaíso - Chile

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Origen de Citas Identificadas



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Citas identificadas: Las citas provienen de documentos incluidos en la base de datos de DATACIENCIA

Citas Identificadas: 1.32 %
Citas No-identificadas: 98.68 %

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Citas identificadas: Las citas provienen de documentos incluidos en la base de datos de DATACIENCIA

Citas Identificadas: 1.32 %
Citas No-identificadas: 98.68 %

Financiamiento



Fuente
University of Auckland
MBIE-Natural Hazards Research Platform (New Zealand)
MBIE-Natural
Saleh Alazmi

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Agradecimientos



Agradecimiento
This research has been funded by the MBIE-Natural Hazards Research Platform (New Zealand), Grant Number: C05X0907. The authors thank Andrew George, Bunpor Taing, Hakshay Kumar, and Matthew Richards, undergraduate students in Civil Engineering at The University of Auckland, for the development of the ACH BIM model; Bruce Rooke for the software development; and Gujun Pu, Mohammed Adel Abdelmegid, Ashkan Mohajeri Naraghi, Rehan Masood, and Saleh Alazmi, Ph.D. students in Civil Engineering at The University of Auckland, for data collection and analysis.
This research has been funded by the MBIE-Natural Hazards Research Platform (New Zealand), Grant Number: C05X0907. The authors thank Andrew George, Bunpor Taing, Hakshay Kumar, and Matthew Richards, undergraduate students in Civil Engineering at The University of Auckland, for the development of the ACH BIM model; Bruce Rooke for the software development; and Gujun Pu, Mohammed Adel Abdelmegid, Ashkan Mohajeri Naraghi, Rehan Masood, and Saleh Alazmi, Ph.D. students in Civil Engineering at The University of Auckland, for data collection and analysis.

Muestra la fuente de financiamiento declarada en la publicación.