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

Research output: Contribution to journalConference articlepeer-review

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 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.

Original languageEnglish
Article number309
JournalIS and T International Symposium on Electronic Imaging Science and Technology
Volume34
Issue number2
DOIs
Publication statusPublished - 2022
EventIS and T International Symposium on Electronic Imaging: 33rd Stereoscopic Displays and Applications, SDA 2022 - Virtual, Online
Duration: 2022 Jan 172022 Jan 26

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Human-Computer Interaction
  • Software
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics

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