Review of Virtual Reality Technology: An Ergonomic Approach and Current Challenges

França, Ana Carol Pontes de; Soares, Marcelo M.

doi:10.1007/978-3-319-60582-1_6

Cited by 12 publications

(17 citation statements)

References 2 publications

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“….., X N −1 (the list of the coefficients of a finite combination of complex sinusoids, ordered by their frequencies). The DFT function is shown in equation (6). Figure 4 shows the PSD results for the user.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Enabling VR to reach its potential, will largely depend on our ability to solve the substantial and enduring problem of motion sickness, which can lead to nausea, dizziness, disorientation, fatigue, and instability. The major cause of motion sickness is undesirable outcomes of hardware and software insufficiency, including low resolution and refresh rates of the image, non-ergonomic interactions, and inappropriate navigation modes [6]- [8]. A common tool for measuring motion sickness in VR is the Simulator Sickness Questionnaire (SSQ).…”

Section: Introductionmentioning

confidence: 99%

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Using EEG and Deep Learning to Predict Motion Sickness Under Wearing a Virtual Reality Device

et al. 2020

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Virtual Reality (VR) research has been widely applied in many fields. VR promises to deliver the experience that is beyond the user's imagination. One of the advantages of VR is the feeling it gives of being there. VR can provide experiences impossible in the real world, such as flying, diving in deep water, exploring outer space, or living with dinosaurs. Despite the improvements in the software and hardware, the problem of motion sickness remains. We implement a deep learning model to train and predict motion sickness. A questionnaire is a well-known method to measure motion sickness. The weakness of the questionnaire is the measurement carried out after the user experiences motion sickness symptoms. By using the deep learning and EEG, the system will learn and classify motion sickness. The system learns the user's EEG pattern when they begin to feel the sickness symptoms. The system will be trained using deep learning to identify the sickness patterns in the future. By the EEG patterns, the system can predict the sickness symptoms before it occurs. Our model outperforms traditional models in loss values, accuracy, and F-measure metrics in Roller Coaster. With other datasets, our model also performs well. Our model can achieve 82.83% accuracy from the dataset. We also found that the time steps to predict motion sickness during 5 minute periods is a suitable configuration.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using EEG and Deep Learning to Predict Motion Sickness Under Wearing a Virtual Reality Device

et al. 2020

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“…Furthermore, a predominant concern for the implementation of VR in cognitive sciences is the presence of VRISE (Bohil et al, 2011;de França and Soares, 2017;Palmisano et al, 2017), which may compromise health and safety standards (Parsons et al, 2018), as well as the reliability of cognitive (Nalivaiko et al, 2015), physiological (Nalivaiko et al, 2015), and neuroimaging data (Arafat et al, 2018;Gavgani et al, 2018). Equally, the high cost of VR software development may additionally deter the adoption of VR as a research tool in cognitive sciences (Slater, 2018).…”

Section: Vr Software Development In Cognitive Sciencesmentioning

confidence: 99%

“…VR has great potential as an effective telemedicine tool that may resolve the current methodological problem of ecological validity (Rizzo et al, 2004;Bohil et al, 2011;Parsons, 2015;Parsons et al, 2018). However, the appropriateness of VR, especially for head-mounted display (HMD) systems, is still controversial (Bohil et al, 2011;de França and Soares, 2017;Palmisano et al, 2017). The principal concern is the adverse symptomatology (i.e., nausea, dizziness, disorientation, fatigue, and instability) which stems from the implementation of VR systems (Bohil et al, 2011;de França and Soares, 2017;Palmisano et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…However, the appropriateness of VR, especially for head-mounted display (HMD) systems, is still controversial (Bohil et al, 2011;de França and Soares, 2017;Palmisano et al, 2017). The principal concern is the adverse symptomatology (i.e., nausea, dizziness, disorientation, fatigue, and instability) which stems from the implementation of VR systems (Bohil et al, 2011;de França and Soares, 2017;Palmisano et al, 2017). These adverse VR induced symptoms and effects (VRISE) endanger the health and safety of the users (Parsons et al, 2018), decrease reaction times and overall cognitive performance (Nalivaiko et al, 2015), while increasing body temperature and heart rates (Nalivaiko et al, 2015), cerebral blood flow and oxyhemoglobin concentration (Gavgani et al, 2018), brain activity (Arafat et al, 2018), and the connectivity between brain regions (Toschi et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Guidelines for the Development of Immersive Virtual Reality Software for Cognitive Neuroscience and Neuropsychology: The Development of Virtual Reality Everyday Assessment Lab (VR-EAL), a Neuropsychological Test Battery in Immersive Virtual Reality

Kourtesis

Korre

Collina

et al. 2020

Front. Comput. Sci.

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Virtual reality (VR) head-mounted displays (HMD) appear to be effective research tools, which may address the problem of ecological validity in neuropsychological testing. However, their widespread implementation is hindered by VR induced symptoms and effects (VRISE) and the lack of skills in VR software development. This study offers guidelines for the development of VR software in cognitive neuroscience and neuropsychology, by describing and discussing the stages of the development of Virtual Reality Everyday Assessment Lab (VR-EAL), the first neuropsychological battery in immersive VR. Techniques for evaluating cognitive functions within a realistic storyline are discussed. The utility of various assets in Unity, software development kits, and other software are described so that cognitive scientists can overcome challenges pertinent to VRISE and the quality of the VR software. In addition, this pilot study attempts to evaluate VR-EAL in accordance with the necessary criteria for VR software for research purposes. The VR neuroscience questionnaire (VRNQ; Kourtesis et al., 2019b) was implemented to appraise the quality of the three versions of VR-EAL in terms of user experience, game mechanics, in-game assistance, and VRISE. Twenty-five participants aged between 20 and 45 years with 12-16 years of full-time education evaluated various versions of VR-EAL. The final version of VR-EAL achieved high scores in every sub-score of the VRNQ and exceeded its parsimonious cut-offs. It also appeared to have better in-game assistance and game mechanics, while its improved graphics substantially Kourtesis et al. VR Research Software Development increased the quality of the user experience and almost eradicated VRISE. The results substantially support the feasibility of the development of effective VR research and clinical software without the presence of VRISE during a 60-min VR session.

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AR In Fashion Industry

Ranka

Mehta

Chopra

2022

2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)

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Review of Virtual Reality Technology: An Ergonomic Approach and Current Challenges

Cited by 12 publications

References 2 publications

Using EEG and Deep Learning to Predict Motion Sickness Under Wearing a Virtual Reality Device

Using EEG and Deep Learning to Predict Motion Sickness Under Wearing a Virtual Reality Device

Guidelines for the Development of Immersive Virtual Reality Software for Cognitive Neuroscience and Neuropsychology: The Development of Virtual Reality Everyday Assessment Lab (VR-EAL), a Neuropsychological Test Battery in Immersive Virtual Reality

AR In Fashion Industry

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