Neural Basis Analysis of Firefighters’ Wayfinding Performance via Functional Near-Infrared Spectroscopy
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 4
Abstract
Disorientation has been identified as one of the leading causes of firefighter injuries and fatalities. In search and rescue tasks, firefighters have to build up a general understanding of an unfamiliar space with limited time and limited information at hand. These two factors together lead to firefighters’ insufficient development of spatial knowledge that could induce firefighters’ disorientation. Therefore, there is a pressing need to better understand how and why different types of spatial information, including landmark, route, and survey information, affect the development of firefighters’ spatial memory of unfamiliar environments. This study examined functional brain connectivity associated with different forms of spatial information that regulate firefighters’ wayfinding performance. We conducted a virtual reality (VR) experiment to investigate the brain-based connectivity changes during wayfinding tasks. To achieve the research goal, we recruited 40 firefighters from the Bryan Fire Department in College Station, Texas. The Firefighters were requested to find three missing victims in a VR office maze, given different wayfinding information, including landmarks, routes, and maps. Functional near-infrared spectroscopy (fNIRS) was used to analyze firefighters’ cerebral hemodynamic responses associated with neural activation and connectivity patterns. The results showed that the firefighters had better task performance when they used route or survey spatial information than when they used other information. Survey information (maps) led to higher neural activation and less-desired neural connectivity patterns than did landmark and route information. Although maps provided complete spatial information about the building layouts in the experiment, they also burdened firefighters’ cognitive processes and required additional effort in memory retention and path planning. Thus, survey information did not result in better performance as suggested by previous studies. The findings are expected to help fire departments design better training protocols and inspire the design of cognition-driven personalized wayfinding systems for firefighters.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors appreciate the participating firefighters from the Bryan Fire Department for giving their valuable time to participate in the experiment. This study is sponsored by NIST (60NANB18D152) and the National Science Foundation (NSF 1937878). Any findings, conclusions, or opinions are those of the authors and do not reflect the views of NIST or NSF.
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Journal of Computing in Civil Engineering
Volume 36 • Issue 4 • July 2022
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© 2022 American Society of Civil Engineers.
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Received: Jul 11, 2021
Accepted: Mar 10, 2022
Published online: Apr 30, 2022
Published in print: Jul 1, 2022
Discussion open until: Sep 30, 2022
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