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VERSION:2.0
PRODID:Linklings LLC
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TZID:America/Phoenix
X-LIC-LOCATION:America/Phoenix
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TZOFFSETFROM:-0700
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TZNAME:MST
DTSTART:19700101T000000
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BEGIN:VEVENT
DTSTAMP:20241014T203103Z
LOCATION:McArthur Ballroom
DTSTART;TZID=America/Phoenix:20240912T173000
DTEND;TZID=America/Phoenix:20240912T183000
UID:HFESAM_ASPIRE - Presented by HFES_sess108_POS349@linklings.com
SUMMARY:117. Workload Estimation Using Facial Expression Analysis
DESCRIPTION:Poster\n\nKayla Riegner and Christopher Mikulski (US Army Grou
 nd Vehicle System Center)\n\nExisting techniques to measure workload are i
 ll-suited in austere operational environments found in the military or law
  enforcement.  Novel techniques to approximate workload by analyzing facia
 l expressions during task execution from camera video feeds, positioned to
  capture the frontal view of crew member faces, is one potential approach 
 to address this gap.  Results from data collection are aimed at understand
 ing the degree to which emotional valence and arousal is correlated to wor
 kload. A secondary data collection helped understand how perception, atten
 tion, and memory affect the correlation of emotional arousal and valence t
 o workload. This research acquired knowledge that could eventually be appl
 ied to algorithms for task switching or to any future human subject study 
 (virtual or field) with an objective to improve manned and unmanned teams.
 \n\nTrack: Aging, Augmented Cognition, Children's Issues, Communications, 
 Cybersecurity, Education, Environmental Design, General Sessions, Human AI
  Robot Teaming (AI), Macroergonomics, Occupational Ergonomics, Student For
 um, Surface Transportation, Sustainability, System Development
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