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DTSTART:19700308T020000
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DTSTART:19701101T020000
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BEGIN:VEVENT
DTSTAMP:20251016T135126Z
LOCATION:Grand Hall L
DTSTART;TZID=America/Chicago:20251016T115000
DTEND;TZID=America/Chicago:20251016T121000
UID:HFESAM_ASPIRE 2025_sess159_LECT427@linklings.com
SUMMARY:Heads Up! Using Head Sway Data to Predict VR Motion Sickness
DESCRIPTION:Morgan Sinko, Thomas Ferris, Michael Do, and Sarvesh Navale (T
 exas A&M University)\n\nPrevious exposure to cybersickness is regularly ci
 ted as a reason for not wanting to adopt VR platforms (Laurell et al., 201
 9). This study evaluated how changes to a user’s head motions, both in rot
 ation and position, could be used as a predictor of cybersickness. \n\nThi
 s study examined data from 36 individuals who each participated in four tr
 ials of data collection. Each trial involved one of four different locomot
 ion techniques. The order of the locomotion conditions was balanced across
  participants. Head sway data was collected, comparing it with the subject
 ive assessments gathered. These were analyzed by splitting the population 
 into two groups. Those who proved susceptible to cybersickness and those w
 ho were not. Participant’s data were passed through a principal component 
 analysis (PCA) to determine which factors had the highest correlation with
  the VRSQ scores.\nPreliminary findings showed confirmation of the anecdot
 al evidence, and showed a decrease in variance and range of head motion al
 ong the X (pitch) axis as VRSQ scores grew. Preliminary data also showed t
 hat total magnitude of head motion along specific axes also decreased as V
 RSQ scores rose.\nThis model can be used in order to further analyze the c
 auses of cybersickness within VR and AR applications.\n\nTrack: Extended R
 eality\n\nSession Chairs: Amelia Warden (University of Michigan) and Emily
  Fang (North Carolina State University)\n\n
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