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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20251016T135140Z
LOCATION:Riverside East
DTSTART;TZID=America/Chicago:20251015T173000
DTEND;TZID=America/Chicago:20251015T183000
UID:HFESAM_ASPIRE 2025_sess138_POST274@linklings.com
SUMMARY:Evaluating the performance of eye movement detection algorithms ac
 ross tasks that elicit different eye movement types in virtual reality.
DESCRIPTION:Junehyung Lee, Ricardo Palma Fraga, and Ziho Kang (University 
 of Oklahoma)\n\nVirtual Reality (VR) technology offers promising tools for
  Human Factors and Ergonomics research, such as eye tracking capabilities 
 that enable researchers to collect participants’ eye movements while compl
 eting a task. To analyze eye movement data, researchers commonly apply eye
  detection algorithms to identify eye movements, such as eye fixations, sa
 ccades, and smooth pursuits, that occurred throughout the task. However, a
  task might elicit all or only some of these eye movements from participan
 ts, which can make selecting an appropriate algorithm a challenge, as many
  algorithms are designed to only identify specific eye movement types. As 
 a result, the present exploratory study evaluated the performance of two a
 lgorithms (I-VVT and I-VDT), designed to identify eye fixations, saccades,
  and smooth pursuits, across two tasks that elicited smaller subsets of ey
 e movement types in VR. Specifically,  one task elicited eye fixations and
  saccades, while the other only elicited smooth pursuits. Our results show
 ed that the I-VDT algorithm outperformed the I-VVT algorithm. However, nei
 ther algorithm accurately identified all the eye movements that occurred, 
 as performance had to be balanced across tasks. Thus, future research shou
 ld investigate whether multiple algorithms could be applied to identify di
 fferent eye movements within a task to increase performance.\n\n
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