TY - GEN
T1 - Designing real-time, continuous emotion annotation techniques for 360° VR videos
AU - Xue, Tong
AU - Ghosh, Surjya
AU - Ding, Gangyi
AU - El Ali, Abdallah
AU - Cesar, Pablo
N1 - Publisher Copyright:
© 2020 Owner/Author.
PY - 2020/4/25
Y1 - 2020/4/25
N2 - With the increasing availability of head-mounted displays (HMDs) that show immersive 360° VR content, it is important to understand to what extent these immersive experiences can evoke emotions. Typically to collect emotion ground truth labels, users rate videos through post-experience self-reports that are discrete in nature. However, post-stimuli self-reports are temporally imprecise, especially after watching 360° videos. In this work, we design six continuous emotion annotation techniques for the Oculus Rift HMD aimed at minimizing workload and distraction. Based on a co-design session with six experts, we contribute HaloLight and DotSize, two continuous annotation methods deemed unobtrusive and easy to understand. We discuss the next challenges for evaluating the usability of these techniques, and reliability of continuous annotations.
AB - With the increasing availability of head-mounted displays (HMDs) that show immersive 360° VR content, it is important to understand to what extent these immersive experiences can evoke emotions. Typically to collect emotion ground truth labels, users rate videos through post-experience self-reports that are discrete in nature. However, post-stimuli self-reports are temporally imprecise, especially after watching 360° videos. In this work, we design six continuous emotion annotation techniques for the Oculus Rift HMD aimed at minimizing workload and distraction. Based on a co-design session with six experts, we contribute HaloLight and DotSize, two continuous annotation methods deemed unobtrusive and easy to understand. We discuss the next challenges for evaluating the usability of these techniques, and reliability of continuous annotations.
KW - 360 video
KW - Continuous
KW - Emotion annotation
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=85090196419&partnerID=8YFLogxK
U2 - 10.1145/3334480.3382895
DO - 10.1145/3334480.3382895
M3 - Conference contribution
AN - SCOPUS:85090196419
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI EA 2020 - Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2020 ACM CHI Conference on Human Factors in Computing Systems, CHI EA 2020
Y2 - 25 April 2020 through 30 April 2020
ER -