Bethany K. Bracken1, Aaron Winder1, Brandon Hager1, Mica R. Endsley2, and Elena K. Festa3
Presented at BIOSTEC 2023. 16th International Joint Conference on Biomedical Engineering Systems and Technologies (February 2023)
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.
1 Charles River Analytics
2 SA Technologies, LLC
3 Brown University
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