Advertisement
Published in:
2019 | OriginalPaper | Chapter
Towards Automated Pain Detection in Children Using Facial and Electrodermal Activity
Authors : Xiaojing Xu, Büşra Tuğçe Susam, Hooman Nezamfar, Damaris Diaz, Kenneth D. Craig, Matthew S. Goodwin, Murat Akcakaya, Jeannie S. Huang, Virginia R. de Sa
Published in: Artificial Intelligence in Health
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Abstract
Accurately determining pain levels in children is difficult, even for trained professionals and parents. Facial activity and electrodermal activity (EDA) provide rich information about pain, and both have been used in automated pain detection. In this paper, we discuss preliminary steps towards fusing models trained on video and EDA features respectively. We compare fusion models using original video features and those using transferred video features which are less sensitive to environmental changes. We demonstrate the benefit of the fusion and the transferred video features with a special test case involving domain adaptation and improved performance relative to using EDA and video features alone.