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Review on visualization technology in simulation training system for major natural disasters
Major natural disasters have occurred frequently in the last few years, resulting in increased loss of life and economic damage. Most emergency responders do not have first-hand experience with major natural disasters, and thus, there is an urgent need for pre-disaster training. Due to the scenes un...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8923969/ https://www.ncbi.nlm.nih.gov/pubmed/35308193 http://dx.doi.org/10.1007/s11069-022-05277-z |
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author | Li, Ning Sun, Na Cao, Chunxia Hou, Shike Gong, Yanhua |
author_facet | Li, Ning Sun, Na Cao, Chunxia Hou, Shike Gong, Yanhua |
author_sort | Li, Ning |
collection | PubMed |
description | Major natural disasters have occurred frequently in the last few years, resulting in increased loss of life and economic damage. Most emergency responders do not have first-hand experience with major natural disasters, and thus, there is an urgent need for pre-disaster training. Due to the scenes unreality of traditional emergency drills, the failure to appeal to the target audience and the novel coronavirus pandemic, people are forced to maintain safe social distancing. Therefore, it is difficult to carry out transregional or transnational emergency drills in many countries under the lockdown. There is an increasing demand for simulation training systems that use virtual reality, augmented reality, and mixed reality visualization technologies to simulate major natural disasters. The simulation training system related to natural disasters provides a new way for popular emergency avoidance science education and emergency rescue personnel to master work responsibilities and improve emergency response capabilities. However, to our knowledge, there is no overview of the simulation training system for major natural disasters. Hence, this paper uncovers the visualization techniques commonly used in simulation training systems, and compares, analyses and summarizes the architecture and functions of the existing simulation training systems for different emergency phases of common natural disasters. In addition, the limitations of the existing simulation training system in practical applications and future development directions are discussed to provide reference for relevant researchers to better understand the modern simulation training system. |
format | Online Article Text |
id | pubmed-8923969 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-89239692022-03-16 Review on visualization technology in simulation training system for major natural disasters Li, Ning Sun, Na Cao, Chunxia Hou, Shike Gong, Yanhua Nat Hazards (Dordr) Review Article Major natural disasters have occurred frequently in the last few years, resulting in increased loss of life and economic damage. Most emergency responders do not have first-hand experience with major natural disasters, and thus, there is an urgent need for pre-disaster training. Due to the scenes unreality of traditional emergency drills, the failure to appeal to the target audience and the novel coronavirus pandemic, people are forced to maintain safe social distancing. Therefore, it is difficult to carry out transregional or transnational emergency drills in many countries under the lockdown. There is an increasing demand for simulation training systems that use virtual reality, augmented reality, and mixed reality visualization technologies to simulate major natural disasters. The simulation training system related to natural disasters provides a new way for popular emergency avoidance science education and emergency rescue personnel to master work responsibilities and improve emergency response capabilities. However, to our knowledge, there is no overview of the simulation training system for major natural disasters. Hence, this paper uncovers the visualization techniques commonly used in simulation training systems, and compares, analyses and summarizes the architecture and functions of the existing simulation training systems for different emergency phases of common natural disasters. In addition, the limitations of the existing simulation training system in practical applications and future development directions are discussed to provide reference for relevant researchers to better understand the modern simulation training system. Springer Netherlands 2022-03-16 2022 /pmc/articles/PMC8923969/ /pubmed/35308193 http://dx.doi.org/10.1007/s11069-022-05277-z Text en © The Author(s), under exclusive licence to Springer Nature B.V. 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Review Article Li, Ning Sun, Na Cao, Chunxia Hou, Shike Gong, Yanhua Review on visualization technology in simulation training system for major natural disasters |
title | Review on visualization technology in simulation training system for major natural disasters |
title_full | Review on visualization technology in simulation training system for major natural disasters |
title_fullStr | Review on visualization technology in simulation training system for major natural disasters |
title_full_unstemmed | Review on visualization technology in simulation training system for major natural disasters |
title_short | Review on visualization technology in simulation training system for major natural disasters |
title_sort | review on visualization technology in simulation training system for major natural disasters |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8923969/ https://www.ncbi.nlm.nih.gov/pubmed/35308193 http://dx.doi.org/10.1007/s11069-022-05277-z |
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