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Authors: Bethany Bracken 1 ; Aaron Winder 1 ; Brandon Hager 1 ; Mica Endsley 2 and Elena Festa 3

Affiliations: 1 Charles River Analytics, 625 Mount Auburn St., Cambridge, MA, 02138, U.S.A. ; 2 SA Technologies, LLC, 5301 S. Superstition Mountain Drive, Suite 104377, Gold Canyon, AZ, 85118 U.S.A. ; 3 Brown University, 190 Thayer St, Providence, RI 02912, U.S.A.

Keyword(s): Cognitive Workload, Situation Awareness (SA), Training.

Abstract: To operate effectively across a variety of environments, personnel (e.g., air traffic controllers, pilots, truck drivers, emergency response crews) need to be trained to the point at which their responses are automatic. If their responses require high mental effort when carried out in emergency situations, they may be unable to perform or to establish situation awareness (SA) needed to perform and to keep themselves safe. We have been developing a software application to assess cognitive workload (i.e., mental effort) during task performance using functional near-infrared spectroscopy (fNIRS). Here we present our work toward extending this human state assessment software to include SA. We used a driving task (Crundall & Kroll, 2018; Muela et al., 2021) in which participants saw a clip of someone driving from a first person perspective followed by a Level 3 SA (prediction) question asking what hazard was about to occur. Participants were 22 Brown University undergraduate and medical students (8 females) with an average age of 22.2 (SD=4.7) and 22 Army personnel in one of the U.S. Army installations with an average age of 49 (SD=11). We were able to predict performance on the SA questions using the fNIRS data, at the group level (mean accuracy = 65% in Brown students, 71% in Army personnel, and 65% in the combined datasets). We were also able to predict SA performance of individual participants with a mean accuracy of 69% (range = .45-.88). This adds to the growing literature indicating that neurophysiological information, even when data is acquired at a single location, is useful for predicting individual SA. (More)

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Paper citation in several formats:
Bracken, B.; Winder, A.; Hager, B.; Endsley, M. and Festa, E. (2023). Assessing Situation Awareness (SA) Using Single- or Dual-Location functional Near Infrared Spectroscopy (fNIRS). In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOSIGNALS; ISBN 978-989-758-631-6; ISSN 2184-4305, SciTePress, pages 149-154. DOI: 10.5220/0011610000003414

@conference{biosignals23,
author={Bethany Bracken. and Aaron Winder. and Brandon Hager. and Mica Endsley. and Elena Festa.},
title={Assessing Situation Awareness (SA) Using Single- or Dual-Location functional Near Infrared Spectroscopy (fNIRS)},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOSIGNALS},
year={2023},
pages={149-154},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011610000003414},
isbn={978-989-758-631-6},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOSIGNALS
TI - Assessing Situation Awareness (SA) Using Single- or Dual-Location functional Near Infrared Spectroscopy (fNIRS)
SN - 978-989-758-631-6
IS - 2184-4305
AU - Bracken, B.
AU - Winder, A.
AU - Hager, B.
AU - Endsley, M.
AU - Festa, E.
PY - 2023
SP - 149
EP - 154
DO - 10.5220/0011610000003414
PB - SciTePress