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Volume: 33 | Article ID: art00003
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Predicting virtual reality discomfort
Vasilii Marshev
Jean-Louis de Bougrenet de la Tocnaye
Béatrice Cochener
Vincent Nourrit
  DOI :  10.2352/ISSN.2470-1173.2021.13.ERVR-168  Published Online :  January 2021
Abstract

Purpose: Virtual Reality (VR) headsets are becoming more and more popular and are now standard attractions in many places such as museums and fairs. Although the issues of VR induced cybersickness or eye strain are well known, as well as the associated risks factors, most studies have focused on reducing it or assessing this discomfort rather than predicting it. Since the negative experience of few users can have a strong impact on the product or an event's publicity the aim of the study was to develop a simple questionnaire that could help a user to rapidly and accurately self-assess personal risks of experiencing discomfort before using VR. Methods: 224 subjects (age 30.44±2.62 y.o.) participated to the study. The VR experience was 30 minutes long. During each session, 4 users participated simultaneously. The experience was conducted with HTC Vive. It consisted in being at the bottom of the ocean and observing surroundings. Users could see the other participants' avatars, move in a 12 m2 area and interact with the environment. The experience was designed to produce as little discomfort as possible. Participants filled out a questionnaire which included 11 questions about their personal information (age, gender, experience with VR, etc.), good binocular vision, need for glasses and use of their glasses during the VR session, tendencies to suffer from other conditions (such as motion sickness, migraines) and the level of fatigue before the experiment, designed to assess their susceptibility to cybersickness. The questionnaire also contained three questions through which subjects self-assessed the impact of the session on their level of visual fatigue, headache and nausea, the sum of which produced the subjective estimate of “VR discomfort” (VRD). 5-point Likert scale was used for the questions when possible. The data of 29 participants were excluded from the analysis due to incomplete data. Results: The correlation analysis showed that five questions' responses correlated with the VRD: sex (r = -.19, p = .02 (FDR corrected)), susceptibility to head aches and migraines (r = -.25, p = .002), susceptibility to motion sickness (r = -.18, p = .02), fatigue or a sickness before the session (r = -.26, p < .002), and the stereoscopic vision issues (r = .23, p = .004). A linear regression model of the discomfort with these five questions as predictors (F(5, 194) = 9.19, p < 0.001, R2 = 0.19) showed that only the level of fatigue (beta = .53, p < .001) reached statistical significance. Conclusion: Even though answers to five questions were found to correlate with VR induced discomfort, linear regression showed that only one of them (the level of fatigue) proved to be useful in prediction of the level of the discomfort. The results suggest that a tool whose purpose is to predict VR-induced discomfort can benefit from a combination of subjectve and objective measures. Conclusion: Even though answers to five questions were found to correlate with VR induced discomfort, linear regression showed that only one of them (the level of fatigue) proved to be useful in prediction of the level of the discomfort. The results suggest that a tool whose purpose is to predict VR-induced discomfort can benefit from a combination of subjectve and objective measures.

Journal Title : Electronic Imaging
Publisher Name : Society for Imaging Science and Technology
Publisher Location : IS&T 7003 Kilworth Lane Springfield, VA 22151 USA
Subject Areas :
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 DOI 10.2352/ISSN.2470-1173.2021.13.ERVR-168
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Vasilii Marshev, Jean-Louis de Bougrenet de la Tocnaye, Béatrice Cochener, Vincent Nourrit, "Predicting virtual reality discomfort"  in Proc. IS&T Int’l. Symp. on Electronic Imaging: The Engineering Reality of Virtual Reality,  2021,  pp 168-1 - 168-5,  https://doi.org/10.2352/ISSN.2470-1173.2021.13.ERVR-168

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Copyright © Society for Imaging Science and Technology 2021
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
IS&T 7003 Kilworth Lane Springfield, VA 22151 USA
10.2352/ISSN.2470-1173.2021.13.ERVR-168
2470-1173(20210118)2021:13L.1681;1-
ei24701173_v2021n13_Input/s3.xml
/ist/ei/2021/00002021/00000013/art00003
Articles
Predicting virtual reality discomfort
MarshevVasilii
de Bougrenet de la TocnayeJean-Louis
CochenerBéatrice
NourritVincent
18012021
2021
13
168-1
168-5
2021
Purpose: Virtual Reality (VR) headsets are becoming more and more popular and are now standard attractions in many places such as museums and fairs. Although the issues of VR induced cybersickness or eye strain are well known, as well as the associated risks factors, most studies have focused on reducing it or assessing this discomfort rather than predicting it. Since the negative experience of few users can have a strong impact on the product or an event's publicity the aim of the study was to develop a simple questionnaire that could help a user to rapidly and accurately self-assess personal risks of experiencing discomfort before using VR. Methods: 224 subjects (age 30.44±2.62 y.o.) participated to the study. The VR experience was 30 minutes long. During each session, 4 users participated simultaneously. The experience was conducted with HTC Vive. It consisted in being at the bottom of the ocean and observing surroundings. Users could see the other participants' avatars, move in a 12 m2 area and interact with the environment. The experience was designed to produce as little discomfort as possible. Participants filled out a questionnaire which included 11 questions about their personal information (age, gender, experience with VR, etc.), good binocular vision, need for glasses and use of their glasses during the VR session, tendencies to suffer from other conditions (such as motion sickness, migraines) and the level of fatigue before the experiment, designed to assess their susceptibility to cybersickness. The questionnaire also contained three questions through which subjects self-assessed the impact of the session on their level of visual fatigue, headache and nausea, the sum of which produced the subjective estimate of “VR discomfort” (VRD). 5-point Likert scale was used for the questions when possible. The data of 29 participants were excluded from the analysis due to incomplete data. Results: The correlation analysis showed that five questions' responses correlated with the VRD: sex (r = -.19, p = .02 (FDR corrected)), susceptibility to head aches and migraines (r = -.25, p = .002), susceptibility to motion sickness (r = -.18, p = .02), fatigue or a sickness before the session (r = -.26, p < .002), and the stereoscopic vision issues (r = .23, p = .004). A linear regression model of the discomfort with these five questions as predictors (F(5, 194) = 9.19, p < 0.001, R2 = 0.19) showed that only the level of fatigue (beta = .53, p < .001) reached statistical significance. Conclusion: Even though answers to five questions were found to correlate with VR induced discomfort, linear regression showed that only one of them (the level of fatigue) proved to be useful in prediction of the level of the discomfort. The results suggest that a tool whose purpose is to predict VR-induced discomfort can benefit from a combination of subjectve and objective measures. Conclusion: Even though answers to five questions were found to correlate with VR induced discomfort, linear regression showed that only one of them (the level of fatigue) proved to be useful in prediction of the level of the discomfort. The results suggest that a tool whose purpose is to predict VR-induced discomfort can benefit from a combination of subjectve and objective measures.
VRVirtual realityCyber sickness