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Electronic Imaging
Article . 2022 . Peer-reviewed
Data sources: Crossref
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Conference object . 2022
License: CC BY
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Conference object . 2024
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Simulation-based virtual reality training for firefighters

Authors: Mohamed Saifeddine Hadj Sassi; Federica Battisti; Marco Carli;

Simulation-based virtual reality training for firefighters

Abstract

Simulation-based training is used to improve learners’ skills and enhance their knowledge. Recently, virtual reality technology has been exploited in simulation mainly for training purposes, to enable learning while performing simulated activities that are dangerous or even impossible to be simulated in the real world. In this context, we present a simulation-based firefighter training for an earthquake scenario developed in collaboration with national Italian firefighters rescue units (Italian “Istituto Superiore Antincendi”). The proposed training model is based on a virtual reality solution and foresees a novel interaction and game model developed specifically for training first-responders. The simulator environment is a head-mounted display where the learner interacts with objects and performs specific tasks. The performed test show that the use of virtual reality can improve the effectiveness of training. Indeed, trainees show a better perception of the scene which is reflected in a faster response in the real situation. The proposed training system can help the firefighter by providing adequate information on how to deal with risks.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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