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A SWOT Analysis of the Field of Virtual Reality for Firefighter Training.
Engelbrecht, Hendrik; Lindeman, Robert W; Hoermann, Simon.
Affiliation
  • Engelbrecht H; HIT Lab NZ, College of Engineering, University of Canterbury, Christchurch, New Zealand.
  • Lindeman RW; HIT Lab NZ, College of Engineering, University of Canterbury, Christchurch, New Zealand.
  • Hoermann S; HIT Lab NZ, College of Engineering, University of Canterbury, Christchurch, New Zealand.
Front Robot AI ; 6: 101, 2019.
Article in En | MEDLINE | ID: mdl-33501116
ABSTRACT
Virtual reality (VR) research has gone through rapid advances and the technology has established itself as a valuable training tool in many domains. While research in the field of emergency response, and more specifically in the field of firefighting, is still catching up, the future potential of VR technology for training is promising. This paper uses the SWOT framework to analyse the strengths, weaknesses, opportunities, and threats immersive VR technology faces in the field of firefighter training. While using VR for training is cost-effective, safe to use and provides the ability to prepare trainees with a large variety of high fidelity training environments, the lack in specialization of the applications for the fire-service sector and issues with technology acceptance and limitations need to be addressed. Looking to current research, there are promising findings that might be directly transferable, creating affective, and multi-sensory experiences for more effective mental and physical training of firefighters in the future. More research is needed to establish methods of skills transfer from VR to real life scenarios and to evaluate the potential risk of frequent training in engaging and physiologically stimulating virtual environments.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Robot AI Year: 2019 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Robot AI Year: 2019 Document type: Article Affiliation country: