TY - GEN
T1 - Visualizing gaze direction to support video coding of social attention for children with autism spectrum disorder
AU - Higuch, Keita
AU - Matsuda, Soichiro
AU - Kamikubo, Rie
AU - Enomoto, Takuya
AU - Sugano, Yusuke
AU - Yamamoto, Junichi
AU - Sato, Yoichi
N1 - Funding Information:
This work was supported by JST CREST Grant Number JP-MJCR14E1 and JPMJCR14E2, Japan. We thank all collaborators in our user studies and dataset for their support.
Publisher Copyright:
© 2018 ACM.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/3/5
Y1 - 2018/3/5
N2 - This paper presents a novel interface to support video coding of social attention in the assessment of children with autism spectrum disorder. Video-based evaluations of social attention during therapeutic activities allow observers to find target behaviors while handling the ambiguity of attention. Despite the recent advances in computer vision-based gaze estimation methods, fully automatic recognition of social attention under diverse environments is still challenging. The goal of this work is to investigate an approach that uses automatic video analysis in a supportive manner for guiding human judgment. The proposed interface displays visualization of gaze estimation results on videos and provides GUI support to allow users to facilitate agreement between observers by defining social attention labels on the video timeline. Through user studies and expert reviews, we show how the interface helps observers perform video coding of social attention and how human judgment compensates for technical limitations of the automatic gaze analysis.
AB - This paper presents a novel interface to support video coding of social attention in the assessment of children with autism spectrum disorder. Video-based evaluations of social attention during therapeutic activities allow observers to find target behaviors while handling the ambiguity of attention. Despite the recent advances in computer vision-based gaze estimation methods, fully automatic recognition of social attention under diverse environments is still challenging. The goal of this work is to investigate an approach that uses automatic video analysis in a supportive manner for guiding human judgment. The proposed interface displays visualization of gaze estimation results on videos and provides GUI support to allow users to facilitate agreement between observers by defining social attention labels on the video timeline. Through user studies and expert reviews, we show how the interface helps observers perform video coding of social attention and how human judgment compensates for technical limitations of the automatic gaze analysis.
KW - Children with ASD
KW - Social attention
KW - Video coding support
UR - http://www.scopus.com/inward/record.url?scp=85045453140&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045453140&partnerID=8YFLogxK
U2 - 10.1145/3172944.3172960
DO - 10.1145/3172944.3172960
M3 - Conference contribution
AN - SCOPUS:85045453140
T3 - International Conference on Intelligent User Interfaces, Proceedings IUI
SP - 571
EP - 582
BT - IUI 2018 - Proceedings of the 23rd International Conference on Intelligent User Interfaces
PB - Association for Computing Machinery
T2 - 23rd ACM International Conference on Intelligent User Interfaces, IUI 2018
Y2 - 7 March 2018 through 11 March 2018
ER -