SD&A 2022: Evaluation and estimation of discomfort during continuous work ...
This presentation was delivered at the 33d annual Stereoscopic Displays and Applications conference (20 January 2022) held online. For more information see: http://stereoscopic.org
Title: Evaluation and estimation of discomfort during continuous work with mixed reality systems by deep learning [SD&A-309]
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.
Speaker: Yoshihiro Banchi, Waseda University (Japan)
© 2022, Society for Imaging Science and Technology (IS&T). Video editing by Jade Woods.