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PRODID:Linklings LLC
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TZID:America/Phoenix
X-LIC-LOCATION:America/Phoenix
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DTSTART:19700101T000000
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BEGIN:VEVENT
DTSTAMP:20241014T203102Z
LOCATION:McArthur Ballroom
DTSTART;TZID=America/Phoenix:20240911T173000
DTEND;TZID=America/Phoenix:20240911T183000
UID:HFESAM_ASPIRE - Presented by HFES_sess107_POS363@linklings.com
SUMMARY:70. Using Neural Data to Classify Workload: A Refresh of the SynWi
 n Task Battery
DESCRIPTION:Poster\n\nOlivia Fox Cotton (Aptima, Inc); Lisa Lucia, Justin 
 Morgan, Jordan Coker, and Matthew Ewer (Aptima, Inc.); and Joseph Geeseman
  (U.S. Navy)\n\nHarnessing neural data collected via functional near-infra
 red spectroscopy (fNIRS) can be a game-changing tool for assessing individ
 ual and team states. However, most neural measurement devices are plagued 
 by barriers like cost, portability, and ease of use by non-experts. Theref
 ore, to aid future research in online state monitoring, we aimed to develo
 p a lightweight fNIRS device and to refresh a previously validated multita
 sk battery. The fNIRS system was tested alongside an updated and contextua
 lized version of the synthetic work environment (SynWin) task battery (Els
 more, 1994) that we call Aviator SynWin. Aviator SynWin, like its predeces
 sor, requires participants to simultaneously perform four unrelated tasks 
 and allows researchers to adjust the timing of concurrent task events to i
 nduce distinct levels of task difficulty. We trained preliminary individua
 lized workload models and found classification accuracy to be low, suggest
 ing that our calibration task did not generalize well to the Aviator SynWi
 n task.\n\nTrack: Aerospace Systems, Cognitive Engineering & Decision Maki
 ng, Computer Systems, Forensics Professional, Health Care, Human Performan
 ce Modeling, Individual Differences in Performance, Perception and Perform
 ance, Product Design, Safety, Training, Usability and System Evaluation, E
 xtended Reality
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