# Towards a Better Understanding of VR Sickness: Physical Symptom Prediction for VR Contents

arXiv: https://arxiv.org/abs/2104.06780 · HTML: https://mlsanity.com/paper/2104.06780/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 14 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [Learning from Simulated and Unsupervised Images through Adversarial Training](https://mlsanity.com/paper/1612.07828/) (2016)
- [Pano2Vid: Automatic Cinematography for Watching 360circ Videos](https://mlsanity.com/paper/1612.02335/) (2016)
- [Measurement of exceptional motion in VR video contents for VR sickness assessment using deep convolutional autoencoder](https://mlsanity.com/paper/1804.03939/) (2018)

## Built on by (most cited first, 1 of 1)

- [VR.net: A Real-world Dataset for Virtual Reality Motion Sickness Research](https://mlsanity.com/paper/2306.03381/) (2023)

## Abstract (arXiv)

We address the black-box issue of VR sickness assessment (VRSA) by evaluating the level of physical symptoms of VR sickness. For the VR contents inducing the similar VR sickness level, the physical symptoms can vary depending on the characteristics of the contents. Most of existing VRSA methods focused on assessing the overall VR sickness score. To make better understanding of VR sickness, it is required to predict and provide the level of major symptoms of VR sickness rather than overall degree of VR sickness. In this paper, we predict the degrees of main physical symptoms affecting the overall degree of VR sickness, which are disorientation, nausea, and oculomotor. In addition, we introduce a new large-scale dataset for VRSA including 360 videos with various frame rates, physiological signals, and subjective scores. On VRSA benchmark and our newly collected dataset, our approach shows a potential to not only achieve the highest correlation with subjective scores, but also to better understand which symptoms are the main causes of VR sickness.