Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards a Better Understanding of VR Sickness: Physical Symptom
  Prediction for VR Contents   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.

10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph