利用面部和皮肤电活动实现儿童疼痛的自动检测
Towards Automated Pain Detection in Children using Facial and Electrodermal Activity.
作者信息
Xu Xiaojing, Susam Büsra Tuğce, Nezamfar Hooman, Diaz Damaris, Craig Kenneth D, Goodwin Matthew S, Akcakaya Murat, Huang Jeannie S, Virginia R de Sa
机构信息
Department of Electrical and Computer Engineering, UC San Diego, La Jolla, CA, USA,
Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
出版信息
CEUR Workshop Proc. 2018 Jul;2142:208-211.
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.
摘要
准确确定儿童的疼痛程度很困难，即使对于训练有素的专业人员和家长来说也是如此。面部活动和皮肤电活动（EDA）提供了有关疼痛的丰富信息，并且两者都已用于自动疼痛检测。在本文中，我们讨论了分别在视频和EDA特征上训练的融合模型的初步步骤。我们比较了使用原始视频特征的融合模型和使用对环境变化不太敏感的转移视频特征的融合模型。我们通过一个涉及领域适应的特殊测试案例展示了融合和转移视频特征的好处，并且相对于单独使用EDA和视频特征，性能有所提高。
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