Overview

In our study, we needed a video database with two different levels of frame rate (10 Hz and 60 Hz) to investigate VR sickness assessment. For this reason, we created a new database of 360-degree videos for experimental validations that consist of 20 videos. The videos are collected from Blend and Vimeo. 25 subjects had participated in the data collection, and we collected physiological signal data (EEG,EKG,GSR) from each subject with individual subjective SSQ score. This web page provides the datasets of our conference paper at 2019 IEEE International Conference on Image Processing (ICIP) [1], so that researchers can repeat our experiments on our datasets. The dataset is for research purposes only. If you use our datasets, please cite the paper [1].

360-degree Video Dataset

We collected 10 different 360-degree videos that include various scenes from Vimeo and Blend as a benchmark. The collected videos are preprocessed so that each video can be displayed with two different frame rates (10Hz, 60Hz). The dataset consists of 20 videos in total. We conducted extensive subjective experiments, and each test video was presented for 180 seconds

Video datasets :  [1]   [2]   [3]   [4]   [5]   [6]   [7]   [8]   [9]   [10]

Subjective SSQ Score

In subjective assessment experiments, LG 32-inches curved UHD monitor was used for displaying 360-degree videos. Its display resolution is 2160 × 1200 pixels and the diagonal field of view is 110 degree. The experimental subjects are advised to watch videos at 40~45 cm distance from the display monitor. A total of 25 subjects, aged from 20 to 30, participated in our subjective experiments under the approval of KAIST Institutional Review Board (IRB). The participants in our experiment do not have health problems such as immature development of visual-vestibular sensors, vestibular dysfunction or oculomotor dysfunction, compared to children and older people. Subjects have normal or corrected-to-normal vision and minimum stereopsis of 60 arcsec. All experimental environments followed the guideline as per the recommendations of ITU-R BT.500-13 and BT.2021.

Subjective SSQ score datasets : [Link]

Corresponding Physiological Signals Dataset

At the same time, we measured electroencephalography, skin conductance and heart rate of subjects during our subjective assessment for objective evaluation of VR sickness. Electroencephalography, heart rate and skin conductance were measured using AIM Gen1 and Quick-30 of Cognionics during watching VR contents with 500 Hz sampling rate. In our experiment, to eliminate the sickness caused by continuously watching VR content, before presenting next VR content, we asked subjects to tell about the current degree of VR sickness on a scale of 0 – 20 using fast motion sickness scale (FMS). When they told 0 score (no sickness), we continuously conducted the experiment. Otherwise, we gave the subject additional resting time until they told 0 score for VR sickness. During the subjective assessment experiment, the subjects were allowed to immediately stop and take a break if they feel difficult to continue the experiment due to excessive VR sickness.

Physiological Signals Datasets : [Link]

Reference

If you use the database, please cite as :

[1] Lee, S., Kim, S., Kim, H. G., Kim, M. S., Yun, S., Jeong, B., & Ro, Y. M. (2019, September). Physiological fusion net: Quantifying individual vr sickness with content stimulus and physiological response. In 2019 IEEE International Conference on Image Processing (ICIP) (pp. 440-444). IEEE.

[2] Lee, S., Kim, S., Kim, H. G., & Ro, Y. M. (2021). Assessing Individual VR Sickness through Deep Feature Fusion of VR Video and Physiological Response. IEEE Transactions on Circuits and Systems for Video Technology.