Citation: Park, S.; Ha, J.; Kim, L.
Effect of Visually Induced Motion
Sickness from Head-Mounted
Display on Cardiac Activity. Sensors
2022, 22, 6213. https://doi.org/
10.3390/s22166213
Academic Editors: Zhihan Lv, Kai Xu
and Zhigeng Pan
Received: 3 July 2022
Accepted: 15 August 2022
Published: 18 August 2022
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Article
Effect of Visually Induced Motion Sickness from Head-Mounted
Display on Cardiac Activity
Sangin Park
1
, Jihyeon Ha
2,3
and Laehyun Kim
2,4,
*
1
Industry-Academy Cooperation Team, Hanyang University, Seoul 04763, Korea
2
Center for Bionics, Korea Institute of Science and Technology, 5 Hwarang-ro 14-gil, Seongbuk-gu,
Seoul 04763, Korea
3
Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea
4
Department of HY-KIST Bio-Convergence, Hanyang University, Seoul 04763, Korea
* Correspondence: laehyunk@kist.re.kr or dochiss@hanyang.ac.kr; Tel.: +82-10-5443-0551
Abstract:
Head-mounted display (HMD) virtual reality devices can facilitate positive experiences
such as co-presence and deep immersion; however, motion sickness (MS) due to these experiences
hinders the development of the VR industry. This paper proposes a method for assessing MS caused
by watching VR content on an HMD using cardiac features. Twenty-eight undergraduate volunteers
participated in the experiment by watching VR content on a 2D screen and HMD for 12 min each,
and their electrocardiogram signals were measured. Cardiac features were statistically analyzed
using analysis of covariance (ANCOVA). The proposed model for classifying MS was implemented
in various classifiers using significant cardiac features. The results of ANCOVA reveal a significant
difference between 2D and VR viewing conditions, and the correlation coefficients between the
subjective ratings and cardiac features have significant results in the range of
−
0.377 to
−
0.711 (for
SDNN, pNN50, and ln HF) and 0.653 to 0.677 (for ln VLF and ln VLF/ln HF ratio). Among the MS
classification models, the linear support vector machine achieves the highest average accuracy of
91.1% (10-fold cross validation) and has a significant permutation test outcome. The proposed method
can contribute to quantifying MS and establishing viewer-friendly VR by determining its qualities.
Keywords:
visually induced motion sickness; normalized heart rate variability; cardiac activity;
head-mounted display; cognitive load
1. Introduction
Virtual reality (VR) using head-mounted displays (HMDs) has become increasingly
popular for professional and entertainment purposes and contributed to technological
advancement and increased economic activity [
1
,
2
]. VR technology has been used in
many areas, such as military training simulations [
3
], training or education in medical
procedures [
4
], architecture [
5
], manufacturing [
6
], entertainment [
7
], and gaming [
8
].
VR technology can provide an experience that is impossible in the real world and deep
immersion [
9
]. However, the devices trigger motion sickness (MS), including visual fatigue,
nausea, anxiety, and disorientation, in some users [
10
]. Visually induced motion sickness
(VIMS), motion sickness disorder (MSD), and VIMS disorder are defined as vestibular
disorders; however, MS can be experienced by anyone [
11
]. Approximately 33% of the
population is highly susceptible [
12
], and at least 59% of the population has reported
experiencing MS [
13
]. Many developers have attempted to improve software and hardware;
however, the issue of MS remains [
14
]. Consequently, MS is a major obstacle in the
popularization and development of the VR industry [
15
]. Thus, studies on minimizing MS
are necessary, which can contribute to improving the VR user experience and friendliness.
To solve the problem of MS, reliable measurement methods for quantitatively assessing MS
should be established [2,16].
Sensors 2022, 22, 6213. https://doi.org/10.3390/s22166213 https://www.mdpi.com/journal/sensors
