Cyber (motion) sickness in active stereoscopic 3D gaming

Benzeroual, Karim; Allison, Robert S.

doi:10.1109/ic3d.2013.6732090

Cited by 13 publications

(13 citation statements)

References 19 publications

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“…In the study of Benzeroual and Allison (2013), a significant interaction effect between MSSQ score and display mode was observed. Users with higher susceptibility reported greater discomfort when viewing stereoscopic images than monoscopic images.…”

Section: Prior Vr Experiences and Motion Sickness Susceptibilitymentioning

confidence: 91%

“…Among 77 experimental studies, 28 articles investigated the hardware effect on VR sickness (Table 1). Four articles examined more than one device-related factor simultaneously (Benzeroual & Allison, 2013;DiZio & Lackner, 1997;Häkkinen et al, 2002;Sharples et al, 2008). The most frequently studied topic was display-related factors.…”

Section: Hardwarementioning

confidence: 99%

“…Motion sickness susceptibility is an important index for predicting the degree of VR sickness. Several studies showed that a person who was vulnerable to motion sickness was likely to report higher discomfort in VR (Benzeroual & Allison, 2013;Llorach et al, 2014;Stanney et al, 2003). In most cases, Motion History Questionnaire (MHQ) (Kennedy & Graybiel, 1965) or Motion Sickness Susceptibility Questionnaire (MSSQ) (Golding, 1998) were used to quantify one's susceptibility.…”

Section: Prior Vr Experiences and Motion Sickness Susceptibilitymentioning

confidence: 99%

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Virtual Reality Sickness: A Review of Causes and Measurements

Chang

Kim

Yoo

2020

International Journal of Human–Computer Interaction

540

228

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In virtual reality (VR), users can experience symptoms of motion sickness, which is referred to as VR sickness or cybersickness. The symptoms include but are not limited to eye fatigue, disorientation, and nausea, which can impair the VR experience of users. Though many studies have attempted to reduce the discomfort, they produced conflicting results with varying degrees of VR sickness. In particular, a visually improved VR does not necessarily result in decreased VR sickness. To understand these unexpected results, we surveyed the causes of VR sickness and measurement of symptoms. We reorganized the causes of the VR sickness into three major factors (hardware, content, and human factors) and investigated the sub-component of each factor. We then surveyed frequently used measures of VR sickness, both subjective and objective approaches. We also investigated emerging approaches for reducing VR sickness and proposed a multimodal fidelity hypothesis to give an insight into future studies.

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Section: Prior Vr Experiences and Motion Sickness Susceptibilitymentioning

confidence: 91%

Section: Hardwarementioning

confidence: 99%

Section: Prior Vr Experiences and Motion Sickness Susceptibilitymentioning

confidence: 99%

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Virtual Reality Sickness: A Review of Causes and Measurements

Chang

Kim

Yoo

2020

International Journal of Human–Computer Interaction

540

228

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“…Even though a lot of efforts are being made to provide near-immersive experience to the users of video games, [104][105][106] more research is needed to overcome the side effects of 3D gaming, such as nausea and motion sickness. 107,108 With the rapid advancements in medical technologies, there is a similarly high demand for stereoscopic medical imaging devices. 109 In a number of published works, [109][110][111][112][113] we can clearly see that quite a few of the works reviewed in this paper are utilized in medical contexts.…”

Section: Discussionmentioning

confidence: 99%

On the practical applications of objective quality metrics for stereoscopic 3D imaging

Biswas

Appina

Tamboli³

et al. 2021

Applications of Machine Learning 2021

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Objective quality metrics provide cost-efficient methods for quality evaluation, as they are practically algorithms, models, that avoid the necessity of subjective assessment, which is a precise but resource-consuming approach. Their ultimate measure of prediction accuracy fundamentally relies on the correlation between the estimated levels of quality and the actual subjective scores of perceived quality, rated by human individuals. Such metrics have already been developed for every single emerging technology where quality, in general, is relevant. This applies to stereoscopic 3D imaging as well, which is utilized in both industry, healthcare, education and entertainment. In this paper, we introduce an exhaustive analysis regarding the practical applications of objective quality metrics for stereoscopic 3D imaging. Our contribution addresses each and every state-of-the-art objective metric in the scientific literature, separately for image and video quality. The study differentiates the metrics by input requirements and supervision, and examines performance via statistical measures. Machine learning algorithms are particularly emphasized within the paper, such as the Deep Edge and COlor Signal INtegrity Evaluator (DECOSINE) using Segmented Stacked Auto-Encoder (S-SAE), different Convolutional Neural Network (CNN) frameworks, and transfer-learning-based methods like the Xception model, AlexNet, ResNet-18, ImageNet, Caffe, GoogLeNet, and also our very own transfer-learning-based methods. The paper focuses on the actual practical applications of the predictive models, and highlights relevant criteria, along with general feasibility, suitability and usability. The analysis of the investigated use cases also addresses potential future research questions and specifies the appropriate directives for quality-focused, user-centric development.

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“…Factors such as age, gender, emotional and psychological state, health and previous experience with VR can all affect susceptibility to cybersickness (LaViola, 2000;Davis et al, 2014). Some people naturally have a higher degree of cybersickness susceptibility, which might be attributed to the difference in depth perception (Benzeroual and Allison, 2013).…”

Section: Cybersicknessmentioning

confidence: 99%

Investigating the User Experience of Virtual Reality Rehabilitation Solution for Biomechatronics Laboratory and Home Environment

Madshaven

Markseth

Jomås

et al. 2021

Front. Virtual Real.

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Virtual reality (VR) technology is a promising tool in physical rehabilitation. Research indicates that VR-supported rehabilitation is beneficial for task-specific training, multi-sensory feedback, diversified rehabilitation tasks, and patient motivation. Our first goal was to create a biomechatronics laboratory with a VR setup for increasing immersion and a motion platform to provide realistic feedback to patients. The second goal was to investigate possibilities to replicate features of the biomechatronics laboratory in a home-based training system using commercially available components. The laboratory comprises of a motion platform with 6-degrees-of-freedom (Rexroth eMotion), fitted with a load cell integrated treadmill, and an Oculus Quest virtual reality headset. The load cells provide input for data collection, as well as VR motion control. The home-based rehabilitation system consists of a Nintendo Wii Balance Board and an Oculus Rift virtual reality headset. User studies in the laboratory and home environment used direct observation techniques and self-reported attitudinal research methods to assess the solution’s usability and user experience. The findings indicate that the proposed VR solution is feasible. Participants using the home-based system experienced more cybersickness and imbalance compared to those using the biomechatronics laboratory solution. Future studies will look at a setup that is safe for first patient studies, and exercises to improve diagnosis of patients and progress during rehabilitation.

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Cyber (motion) sickness in active stereoscopic 3D gaming

Cited by 13 publications

References 19 publications

Virtual Reality Sickness: A Review of Causes and Measurements

Virtual Reality Sickness: A Review of Causes and Measurements

On the practical applications of objective quality metrics for stereoscopic 3D imaging

Investigating the User Experience of Virtual Reality Rehabilitation Solution for Biomechatronics Laboratory and Home Environment

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