Physiological Fusion Net: Quantifying Individual VR Sickness with Content Stimulus and Physiological Response

Abstract

Quantifying Virtual Reality (VR) sickness is demanded in industry to address viewing safety issue. In this paper, we develop a new method to quantify VR sickness. We propose a novel physiological fusion deep network which estimates individual VR sickness with content stimulus and physiological response. In the proposed framework, content stimulus guider and physiological response guider are devised to effectively represent feature related with VR sickness. Deep stimulus feature from the content stimulus guiders reflects the content sickness tendency while deep physiology feature from the physiological response guider reflects the individual sickness characteristics. By combining those features, VR sickness predictor quantifies individual Simulation Sickness Questionnaires (SSQ) scores. To evaluate the performance of the proposed method, we built a new dataset that consists of 360-degree videos with physiological signals and SSQ scores. Experimental results show that the proposed method achieved meaningful correlation with human subjective scores.