![]() ![]() ![]() The proposed method can contribute to quantifying MS and establishing viewer-friendly VR by determining its qualities. ![]() 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 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). The proposed model for classifying MS was implemented in various classifiers using significant cardiac features. Cardiac features were statistically analyzed using analysis of covariance (ANCOVA). 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. This paper proposes a method for assessing MS caused by watching VR content on an HMD using cardiac features. 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. ![]()
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