Name
Title | ||
|---|---|---|
Towards automated pain detection in children using facial and electrodermal activity. |
Abstract | ||
|---|---|---|
Accurately determining pain levels in children is difficult, even for trained professionals and parents. Facial activity and electro- dermal 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. |
Year | Venue | Keywords |
|---|---|---|
2018 | AIH@IJCAI | Automated Pain Detection,Domain Adaptation,EDA,FACS,Facial Action Units,GSR |
DocType | Citations | PageRank |
Conference | 1 | 0.44 |
References | Authors | |
3 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaojing Xu | 1 | 11 | 6.51 |
| Busra T. Susam | 2 | 4 | 2.06 |
| H Nezamfar | 3 | 17 | 5.64 |
| Kenneth D. Craig | 4 | 4 | 1.73 |
| Damaris Diaz | 5 | 4 | 1.73 |
| Jeannie Huang | 6 | 2 | 1.57 |
| Matthew S. Goodwin | 7 | 178 | 19.63 |
| Murat Akçakaya | 8 | 59 | 21.15 |
| Virginia R. de Sa | 9 | 31 | 8.29 |