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Volume: 34 | Article ID: SDA-309
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Evaluation and estimation of discomfort during continuous work with mixed reality systems by deep learning
  DOI :  10.2352/EI.2022.34.2.SDA-309  Published OnlineJanuary 2022
Abstract
Abstract

Mixed reality systems are often reported to cause user discomfort. Therefore, it is important to estimate the timing at which discomfort occurs and to consider ways to reduce or avoid it. The purpose of this study is to estimate the discomfort of the user while using the MR system. Psychological and physiological indicators during task were measured using the MR system, and a deep learning model was constructed to estimate psychological indicators from physiological indicators. As a result of 4-fold cross-validation, the average F1 value of each discomfort score was 0.602 for 1, 0.555 for 2, and 0.290 for 3. This result suggests that mild discomfort can be detected with a certain degree of accuracy.

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Yoshihiro Banchi, Kento Tsuchiya, Masato Hirose, Ryu Takahashi, Riku Yamashita, Takashi Kawai, "Evaluation and estimation of discomfort during continuous work with mixed reality systems by deep learningin Proc. IS&T Int’l. Symp. on Electronic Imaging: Stereoscopic Displays and Applications,  2022,  pp 309-1 - 309-4,  https://doi.org/10.2352/EI.2022.34.2.SDA-309

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