RCEA-360VR: Real-time, continuous emotion annotation in 360◦ VR videos for collecting precise viewport-dependent ground truth labels

Precise emotion ground truth labels for 360◦ virtual reality (VR) video watching are essential for fne-grained predictions under varying viewing behavior. However, current annotation techniques either rely on post-stimulus discrete self-reports, or real-time, con- tinuous emotion annotations (RCEA) but only for desktop/mobile settings. We present RCEA for 360◦ VR videos (RCEA-360VR), where we evaluate in a controlled study (N=32) the usability of two peripheral visualization techniques: HaloLight and DotSize. We furthermore develop a method that considers head movements when fusing labels. Using physiological, behavioral, and subjective measures, we show that (1) both techniques do not increase users’ workload, sickness, nor break presence (2) our continuous valence and arousal annotations are consistent with discrete within-VR and original stimuli ratings (3) users exhibit high similarity in viewing behavior, where fused ratings perfectly align with intended labels. Our work contributes usable and efective techniques for collecting fne-grained viewport-dependent emotion labels in 360◦ VR

CEAP-360VR: A Continuous Physiological and Behavioral Emotion Annotation Dataset for 360 VR Videos

We develop the CEAP-360VR dataset to address the lack of continuously annotated behavioral and physiological datasets for 360 video VR affective computing. Accordingly, this dataset contains a) questionnaires (SSQ, IPQ, NASA-TLX); b) continuous valence-arousal annotations; c) head and eye movements as well as left and right eye pupil diameters while watching videos; d) peripheral physiological responses (ACC, EDA, SKT, BVP, HR, IBI). Our dataset also concludes the data pre-processing, data validating scripts, along with dataset description and key steps in the stage of data acquisition and pre-processing