Effect of Visually Induced Motion Sickness from Head-Mounted Display on Cardiac Activity
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 conten...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/16/6213 |
| _version_ | 1797408053825372160 |
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| author | Sangin Park Jihyeon Ha Laehyun Kim |
| author_facet | Sangin Park Jihyeon Ha Laehyun Kim |
| author_sort | Sangin Park |
| collection | DOAJ |
| description | 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 <i>ln</i> HF) and 0.653 to 0.677 (for <i>ln</i> VLF and <i>ln</i> VLF/<i>ln</i> 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. |
| first_indexed | 2024-03-09T03:51:48Z |
| format | Article |
| id | doaj.art-b56dd16c80664348b678726a6261275b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T03:51:48Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b56dd16c80664348b678726a6261275b2023-12-03T14:27:15ZengMDPI AGSensors1424-82202022-08-012216621310.3390/s22166213Effect of Visually Induced Motion Sickness from Head-Mounted Display on Cardiac ActivitySangin Park0Jihyeon Ha1Laehyun Kim2Industry-Academy Cooperation Team, Hanyang University, Seoul 04763, KoreaCenter for Bionics, Korea Institute of Science and Technology, 5 Hwarang-ro 14-gil, Seongbuk-gu, Seoul 04763, KoreaCenter for Bionics, Korea Institute of Science and Technology, 5 Hwarang-ro 14-gil, Seongbuk-gu, Seoul 04763, KoreaHead-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 <i>ln</i> HF) and 0.653 to 0.677 (for <i>ln</i> VLF and <i>ln</i> VLF/<i>ln</i> 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.https://www.mdpi.com/1424-8220/22/16/6213visually induced motion sicknessnormalized heart rate variabilitycardiac activityhead-mounted displaycognitive load |
| spellingShingle | Sangin Park Jihyeon Ha Laehyun Kim Effect of Visually Induced Motion Sickness from Head-Mounted Display on Cardiac Activity Sensors visually induced motion sickness normalized heart rate variability cardiac activity head-mounted display cognitive load |
| title | Effect of Visually Induced Motion Sickness from Head-Mounted Display on Cardiac Activity |
| title_full | Effect of Visually Induced Motion Sickness from Head-Mounted Display on Cardiac Activity |
| title_fullStr | Effect of Visually Induced Motion Sickness from Head-Mounted Display on Cardiac Activity |
| title_full_unstemmed | Effect of Visually Induced Motion Sickness from Head-Mounted Display on Cardiac Activity |
| title_short | Effect of Visually Induced Motion Sickness from Head-Mounted Display on Cardiac Activity |
| title_sort | effect of visually induced motion sickness from head mounted display on cardiac activity |
| topic | visually induced motion sickness normalized heart rate variability cardiac activity head-mounted display cognitive load |
| url | https://www.mdpi.com/1424-8220/22/16/6213 |
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