SACA Net: Cybersickness Assessment of Individual Viewers for VR Content via Graph-based Symptom Relation Embedding

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 88
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorLee, Sangminko
dc.contributor.authorKim, Jung Ukko
dc.contributor.authorKim, Hak Guko
dc.contributor.authorKim, Seongyeopko
dc.contributor.authorRo, Yong Manko
dc.identifier.citationEuropean Conference on Computer Vision (ECCV) 2020-
dc.description.abstractRecently, cybersickness assessment for VR content is in demand to deal with viewing safety issues. Assessing physical symptoms of individual viewers is challenging but important to provide detailed and personalized guides for viewing safety. In this paper, we propose a novel symptom-aware cybersickness assessment network (SACA Net) that quantifies physical symptom levels for assessing cybersickness of individual viewers. SACA Net is designed to utilize the relational characteristics of symptoms for complementary effects among relevant symptoms. The proposed network consists of three main parts: a stimulus symptom context guider, a physiological symptom guider, and a symptom relation embedder. The stimulus symptom context guider and the physiological symptom guider extract symptom features from VR content and human physiology, respectively. The symptom relation embedder refines the stimulus-response symptom features to effectively predict cybersickness by embedding relational characteristics with graph formulation. For validation, we utilize two public 360-degree video datasets that contain cybersickness scores and physiological signals. Experimental results show that the proposed method is effective in predicting human cybersickness with physical symptoms. Further, latent relations among symptoms are interpretable by analyzing relational weights in the proposed network.-
dc.publisherEuropean Conference on Computer Vision Committee-
dc.titleSACA Net: Cybersickness Assessment of Individual Viewers for VR Content via Graph-based Symptom Relation Embedding-
dc.citation.publicationnameEuropean Conference on Computer Vision (ECCV) 2020-
dc.identifier.conferencelocationGlasgow, UK-
dc.contributor.localauthorRo, Yong Man-
dc.contributor.nonIdAuthorKim, Hak Gu-
Appears in Collection
EE-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.


  • mendeley


rss_1.0 rss_2.0 atom_1.0