  Virtual Reality (VR) sickness commonly known as cybersickness is one of the
major problems for the comfortable use of VR systems. Researchers have proposed
different approaches for predicting cybersickness from bio-physiological data
(e.g., heart rate, breathing rate, electroencephalogram). However, collecting
bio-physiological data often requires external sensors, limiting locomotion and
3D-object manipulation during the virtual reality (VR) experience. Limited
research has been done to predict cybersickness from the data readily available
from the integrated sensors in head-mounted displays (HMDs) (e.g.,
head-tracking, eye-tracking, motion features), allowing free locomotion and
3D-object manipulation. This research proposes a novel deep fusion network to
predict cybersickness severity from heterogeneous data readily available from
the integrated HMD sensors. We extracted 1755 stereoscopic videos,
eye-tracking, and head-tracking data along with the corresponding self-reported
cybersickness severity collected from 30 participants during their VR gameplay.
We applied several deep fusion approaches with the heterogeneous data collected
from the participants. Our results suggest that cybersickness can be predicted
with an accuracy of 87.77\% and a root-mean-square error of 0.51 when using
only eye-tracking and head-tracking data. We concluded that eye-tracking and
head-tracking data are well suited for a standalone cybersickness prediction
framework.
