Measurement of exceptional motion in VR video contents for VR sickness assessment using deep convolutional autoencoder

Kim, Hak Gu; Baddar, Wissam J.; Lim, Heoun-taek; Jeong, Hyunwook; Ro, Yong Man

doi:10.1145/3139131.3139137

Cited by 51 publications

(19 citation statements)

References 33 publications

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“…The extracted features were regressed onto subjective sickness scores, which were obtained from the deployment of a post-questionnaire. Kim et al [ 27 , 28 ] defined a concept of exceptional motion in VR content, and they predicted it by deploying an auto-encoder. The latent variables of their model were learned from easily downloadable

scenes, and the results showed that the inferred exceptional motion was correlated with cybersickness.…”

Section: Introductionmentioning

confidence: 99%

Cybersickness and Its Severity Arising from Virtual Reality Content: A Comprehensive Study

Son

2022

Sensors

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Virtual reality (VR) experiences often elicit a negative effect, cybersickness, which results in nausea, disorientation, and visual discomfort. To quantitatively analyze the degree of cybersickness depending on various attributes of VR content (i.e., camera movement, field of view, path length, frame reference, and controllability), we generated cybersickness reference (CYRE) content with 52 VR scenes that represent different content attributes. A protocol for cybersickness evaluation was designed to collect subjective opinions from 154 participants as reliably as possible in conjunction with objective data such as rendered VR scenes and biological signals. By investigating the data obtained through the experiment, the statistically significant relationships—the degree that the cybersickness varies with each isolated content factor—are separately identified. We showed that the cybersickness severity was highly correlated with six biological features reflecting brain activities (i.e., relative power spectral densities of Fp1 delta, Fp 1 beta, Fp2 delta, Fp2 gamma, T4 delta, and T4 beta waves) with a coefficient of determination greater than 0.9. Moreover, our experimental results show that individual characteristics (age and susceptibility) are also quantitatively associated with cybersickness level. Notably, the constructed dataset contains a number of labels (i.e., subjective cybersickness scores) that correspond to each VR scene. We used these labels to build cybersickness prediction models and obtain a reliable predictive performance. Hence, the proposed dataset is supposed to be widely applicable in general-purpose scenarios regarding cybersickness quantification.

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scenes, and the results showed that the inferred exceptional motion was correlated with cybersickness.…”

Section: Introductionmentioning

confidence: 99%

Cybersickness and Its Severity Arising from Virtual Reality Content: A Comprehensive Study

Son

2022

Sensors

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“…The study of the literature shows that ML techniques have extensively been used for the QoE prediction of VR videos [36]- [38]. The previous researches in [39]- [42] presents the capability of various ML algorithms to classify the values for QoE-affecting factors, such as video quality and stalling.…”

Section: B Ml-based Qoe Prediction Methodsmentioning

confidence: 99%

Subjective QoE of 360-Degree Virtual Reality Videos and Machine Learning Predictions

et al. 2020

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360-degree video provides an immersive experience to end-users through Virtual Reality (VR) Head-Mounted-Displays (HMDs). However, it is not trivial to understand the Quality of Experience (QoE) of 360-degree video since user experience is influenced by various factors that affect QoE when watching a 360-degree video in VR. This manuscript presents a machine learning-based QoE prediction of 360degree video in VR, considering the two key QoE aspects: perceptual quality and cybersickness. In addition, we proposed two new QoE-affecting factors: user's familiarity with VR and user's interest in 360-degree video for the QoE evaluation. To aim this, we first conduct a subjective experiment on 96 video samples and collect datasets from 29 users for perceptual quality and cybersickness. We design a new Logistic Regression (LR) based model for QoE prediction in terms of perceptual quality. The prediction accuracy of the proposed model is compared against well-known supervised machine-learning algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision Tree (DT) with respect to accuracy rate, recall, f1-score, precision, and mean absolute error (MAE). LR performs well with 86% accuracy, which is in close agreement with subjective opinion. The prediction accuracy of the proposed model is then compared with existing QoE models in terms of perceptual quality. Finally, we build a Neural Network-based model for the QoE prediction in terms of cybersickness. The proposed model performs well against the state of the art QoE prediction methods in terms of cybersickness.

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“…As machine learning technology advanced, researchers have proposed solutions to predict cybersickness. Previous work demonstrated the viability to predict cybersickness (Padmanaban et al 2018;Kim et al 2017;Jin et al 2018). To improve user satisfaction with VR applications, it is important to develop solid objective metrics that can analyze and then predict the level of VR sickness when a user is exposed to VEs.…”

Section: Prediction Of Cybersicknessmentioning

confidence: 99%

A review of cybersickness in head-mounted displays: raising attention to individual susceptibility

2022

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Cybersickness still poses a significant challenge to the widespread usage of virtual reality, leading to different levels of discomfort and potentially breaking the immersive experience. Researchers have attempted to discover the possible fundamental causes of cybersickness for years. Despite the longstanding interest in the research field, inconsistent results have been drawn on the contributing factors and solutions to combating cybersickness. Moreover, little attention has been paid to individual susceptibility. A consolidated explanation remains under development, requiring more empirical studies with robust and reproducible methodologies. This review presents an integrated survey connecting the findings from previous review papers and the state of the art involving empirical studies and participants. A literature review is then presented, focusing on the practical studies of different contributing factors, the pros and cons of measurements, profiles of cybersickness, and solutions to reduce this phenomenon. Our findings suggest a lack of considerations regarding user susceptibility and gender balance in between groups studies. In addition, incongruities among empirical findings raised concerns. We conclude by suggesting points of insights for future empirical investigations.

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Measurement of exceptional motion in VR video contents for VR sickness assessment using deep convolutional autoencoder

Cited by 51 publications

References 33 publications

Cybersickness and Its Severity Arising from Virtual Reality Content: A Comprehensive Study

Cybersickness and Its Severity Arising from Virtual Reality Content: A Comprehensive Study

Subjective QoE of 360-Degree Virtual Reality Videos and Machine Learning Predictions

A review of cybersickness in head-mounted displays: raising attention to individual susceptibility

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