Evaluation and estimation of discomfort during continuous work with Mixed Reality systems by deep learning

Yoshihiro Banchi, Kento Tsuchiya, Masato Hirose, Ryu Takahashi, Riku Yamashita, Takashi Kawai

研究成果: Conference article査読

抄録

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 the 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 0.608 for 1 (Nothing at all), 0.555 for 2 (Slightly Discomfort), and 0.290 for 3 (Very Discomfort). This result suggests that mild discomfort can be detected with a certain degree of accuracy.

本文言語English
論文番号309
ジャーナルIS and T International Symposium on Electronic Imaging Science and Technology
34
2
DOI
出版ステータスPublished - 2022
イベントIS and T International Symposium on Electronic Imaging: 33rd Stereoscopic Displays and Applications, SDA 2022 - Virtual, Online
継続期間: 2022 1月 172022 1月 26

ASJC Scopus subject areas

  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • コンピュータ サイエンスの応用
  • 人間とコンピュータの相互作用
  • ソフトウェア
  • 電子工学および電気工学
  • 原子分子物理学および光学

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