Pain Action Unit Detection in Critically Ill Patients

Existing pain assessment methods in the intensive care unit rely on patient self-report or visual observation by nurses. Patient self-report is subjective and can suffer from poor recall. In the case of non-verbal patients, behavioral pain assessment methods provide limited granularity, are subjective, and put additional burden on already overworked staff. Previous studies have shown the feasibility of autonomous pain expression assessment by detecting Facial Action Units (AUs). However, previous approaches for detecting facial pain AUs are historically limited to controlled environments. In this study, for the first time, we collected and annotated a pain-related AU dataset, Pain-ICU, containing 55,085 images from critically ill adult patients. We evaluated the performance of OpenFace, an open-source facial behavior analysis tool, and the trained AU R-CNN model on our Pain-ICU dataset. Variables such as assisted breathing devices, environmental lighting, and patient orientation with respect to the camera make AU detection harder than with controlled settings. Although OpenFace has shown state-of-the-art results in general purpose AU detection tasks, it could not accurately detect AUs in our Pain-ICU dataset (F1-score 0.42). To address this problem, we trained the AU R-CNN model on our Pain-ICU dataset, resulting in a satisfactory average F1-score 0.77. In this study, we show the feasibility of detecting facial pain AUs in uncontrolled ICU settings.

Towards Automated Pain Detection in Children using Facial and Electrodermal Activity

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.