The effect of water immersion on vection in virtual reality

AbstractResearch about vection (illusory self-motion) has investigated a wide range of sensory cues and employed various methods and equipment, including use of virtual reality (VR). However, there is currently no research in the field of vection on the impact of floating in water while experiencing VR. Aquatic immersion presents a new and interesting method to potentially enhance vection by reducing conflicting sensory information that is usually experienced when standing or sitting on a stable surface. This study compares vection, visually induced motion sickness, and presence among participants experiencing VR while standing on the ground or floating in water. Results show that vection was significantly enhanced for the participants in the Water condition, whose judgments of self-displacement were larger than those of participants in the Ground condition. No differences in visually induced motion sickness or presence were found between conditions. We discuss the implication of this new type of VR experience for the fields of VR and vection while also discussing future research questions that emerge from our findings.

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Features of the Postural Sway Signal as Indicators to Estimate and Predict Visually Induced Motion Sickness in Virtual Reality

International Journal of Human-Computer Interaction ◽

10.1080/10447318.2017.1286767 ◽

2017 ◽

Vol 33 (10) ◽

pp. 771-785 ◽

Cited By ~ 24

Author(s):

Jean-Rémy Chardonnet ◽

Mohammad Ali Mirzaei ◽

Frédéric Mérienne

Keyword(s):

Virtual Reality ◽

Motion Sickness ◽

Postural Sway ◽

Visually Induced Motion Sickness ◽

Induced Motion

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Simulation and Virtual Reality Using Nonlinear Kinematic Parameters as a Means of Predicting Motion Sickness in Real-Time in Virtual Environments

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/00187208211059623 ◽

2021 ◽

pp. 001872082110596

Author(s):

L. James Smart ◽

Anthony Drew ◽

Tyler Hadidon ◽

Max Teaford ◽

Eric Bachmann

Keyword(s):

Virtual Reality ◽

Real Time ◽

Motion Sickness ◽

Prediction Algorithm ◽

Kinematic Parameters ◽

Improve Performance ◽

Wide Range ◽

Behavioral Parameters ◽

Accuracy Study ◽

Induced Motion

Objective This article presents two studies (one simulation and one pilot) that assess a custom computer algorithm designed to predict motion sickness in real-time. Background Virtual reality has a wide range of applications; however, many users experience visually induced motion sickness. Previous research has demonstrated that changes in kinematic (behavioral) parameters are predictive of motion sickness. However, there has not been research demonstrating that these measures can be utilized in real-time applications. Method Two studies were performed to assess an algorithm designed to predict motion sickness in real-time. Study 1 was a simulation study that used data from Smart et al. (2014). Study 2 employed the algorithm on 28 new participants’ motion while exposed to virtual motion. Results Study 1 revealed that the algorithm was able to classify motion sick participants with 100% accuracy. Study 2 revealed that the algorithm could predict if a participant would become motion sick with 57% accuracy. Conclusion The results of the present study suggest that the motion sickness prediction algorithm can predict if an individual will experience motion sickness but needs further refinement to improve performance. Application The algorithm could be used for a wide array of VR devices to predict likelihood of motion sickness with enough time to intervene.

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Use of Smartphone-Based Head-Mounted Display Devices to View a Three-Dimensional Dissection Model in a Virtual Reality Environment: Pilot Questionnaire Study (Preprint)

10.2196/preprints.11921 ◽

2018 ◽

Author(s):

Yoshihito Masuoka ◽

Hiroyuki Morikawa ◽

Takashi Kawai ◽

Toshio Nakagohri

Keyword(s):

Virtual Reality ◽

Motion Sickness ◽

3D Model ◽

Low Cost ◽

Three Dimensional ◽

Visually Induced Motion Sickness ◽

Head Mounted Display ◽

Eye Fatigue ◽

Induced Motion ◽

Observation Environment

BACKGROUND Virtual reality (VR) technology has started to gain attention as a form of surgical support in medical settings. Likewise, the widespread use of smartphones has resulted in the development of various medical applications; for example, Google Cardboard, which can be used to build simple head-mounted displays (HMDs). However, because of the absence of observed and reported outcomes of the use of three-dimensional (3D) organ models in relevant environments, we have yet to determine the effects of or issues with the use of such VR technology. OBJECTIVE The aim of this paper was to study the issues that arise while observing a 3D model of an organ that is created based on an actual surgical case through the use of a smartphone-based simple HMD. Upon completion, we evaluated and gathered feedback on the performance and usability of the simple observation environment we had created. METHODS We downloaded our data to a smartphone (Galaxy S6; Samsung, Seoul, Korea) and created a simple HMD system using Google Cardboard (Google). A total of 17 medical students performed 2 experiments: an observation conducted by a single observer and another one carried out by multiple observers using a simple HMD. Afterward, they assessed the results by responding to a questionnaire survey. RESULTS We received a largely favorable response in the evaluation of the dissection model, but also a low score because of visually induced motion sickness and eye fatigue. In an introspective report on simultaneous observations made by multiple observers, positive opinions indicated clear image quality and shared understanding, but displeasure caused by visually induced motion sickness, eye fatigue, and hardware problems was also expressed. CONCLUSIONS We established a simple system that enables multiple persons to observe a 3D model. Although the observation conducted by multiple observers was successful, problems likely arose because of poor smartphone performance. Therefore, smartphone performance improvement may be a key factor in establishing a low-cost and user-friendly 3D observation environment.

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Effects of whole body movements in using virtual reality headsets on visually induced motion sickness

Journal of Digital Contents Society ◽

10.9728/dcs.2017.18.2.283 ◽

2017 ◽

Vol 18 (2) ◽

pp. 283-291

Author(s):

Sung-ho Kim ◽

Dong-Hee Shin

Keyword(s):

Virtual Reality ◽

Motion Sickness ◽

Whole Body ◽

Body Movements ◽

Visually Induced Motion Sickness ◽

Induced Motion

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A Pilot Study on Electroencephalogram-based Evaluation of Visually Induced Motion Sickness

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2020.64.2.020501 ◽

2020 ◽

Vol 64 (2) ◽

pp. 20501-1-20501-10

Author(s):

Ran Liu ◽

Miao Xu ◽

Yanzhen Zhang ◽

Eli Peli ◽

Alex D. Hwang

Keyword(s):

Virtual Reality ◽

Standard Deviation ◽

Motion Sickness ◽

Driving Simulator ◽

Visually Induced Motion Sickness ◽

Power Spectral ◽

Wearable Eeg ◽

Eeg Data ◽

Induced Motion ◽

Electroencephalogram Eeg

Abstract The most prominent problem in virtual reality (VR) technology is that users may experience motion-sickness-like symptoms when they immerse into a VR environment. These symptoms are recognized as visually induced motion sickness (VIMS) or virtual reality motion sickness. The objectives of this study were to investigate the association between the electroencephalogram (EEG) and subjectively rated VIMS level (VIMSL) and find EEG markers for VIMS evaluation. A VR-based vehicle-driving simulator was used to induce VIMS symptoms, and a wearable EEG device with four electrodes (the Muse) was used to collect EEG data. The results suggest that individual tolerance, susceptibility, and recoverability to VIMS varied largely among subjects; the following markers were shown to be significantly different from no-VIMS and VIMS states (P < 0.05): (1) means of gravity frequency (GF) for theta@FP1, alpha@TP9, alpha@FP2, alpha@TP10, and beta@FP1; (2) standard deviation of GF for alpha@TP9, alpha@FP1, alpha@FP2, alpha@TP10, and alpha@(FP2‐FP1); (3) standard deviation of power spectral entropy for FP1; (4) means of Kolmogorov complexity (KC) for TP9, FP1, and FP2. These results also demonstrate that it is feasible to perform VIMS evaluation using an EEG device with a few electrodes.

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Gender Differences in Visually Induced Motion Sickness

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193120204602602 ◽

2002 ◽

Vol 46 (26) ◽

pp. 2109-2113 ◽

Cited By ~ 10

Author(s):

David A. Graeber ◽

Kay M. Stanney

Keyword(s):

Gender Differences ◽

Motion Sickness ◽

Dropout Rate ◽

Future Research ◽

Questionnaire Data ◽

Simulator Sickness ◽

Visually Induced Motion Sickness ◽

Significant Difference ◽

Induced Motion ◽

Analysis Of Motion

In general, females have been reported to be more susceptible to motion sickness and experience greater malaise. However, conflicting findings suggests that gender differences may be driven by susceptibility rather than gender. In this study, susceptibility was balanced within the genders and treatments to determine if gender differences are due to gender or susceptibility. Analysis of motion history questionnaire data found no significant differences between genders, but did find a significant difference between hi and low susceptibles. Analysis of Simulator Sickness Questionnaire total scores revealed consistent insignificant differences between the genders in all treatments and significant differences between hi and low susceptibles at all sampling points except baseline. Analyzing dropout and percent exposure duration completed data revealed an unsubstantial difference between genders, but did yield a dropout rate 2.3 times higher among high susceptibles compared to low susceptibles. As a result of the aforementioned findings, future research in motion sickness gender differences should account for susceptibility as a subject variable.

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