Painful monitoring: Automatic pain monitoring using the UNBC-McMaster shoulder pain expression archive database

In intensive care units in hospitals, it has been recently shown that enormous improvements in patient outcomes can be gained from the medical staff periodically monitoring patient pain levels. However, due to the burden/stress that the staff are already under, this type of monitoring has been difficult to sustain so an automatic solution could be an ideal remedy. Using an automatic facial expression system to do this represents an achievable pursuit as pain can be described via a number of facial action units (AUs). To facilitate this work, the “University of Northern British Columbia-McMaster Shoulder Pain Expression Archive Database” was collected which contains video of participant's faces (who were suffering from shoulder pain) while they were performing a series of range-of-motion tests. Each frame of this data was AU coded by certified FACS coders, and self-report and observer measures at the sequence level were taken as well. To promote and facilitate research into pain and augmentcurrent datasets, we have publicly made available a portion of this database, which includes 200 sequences across 25 subjects, containing more than 48,000 coded frames of spontaneous facial expressions with 66-point AAM tracked facial feature landmarks. In addition to describing the data distribution, we give baseline pain and AU detection results on a frame-by-frame basis at the binary-level (i.e. AU vs. no-AU and pain vs. no-pain) using our AAM/SVM system. Another contribution we make is classifying pain intensities at the sequence-level by using facial expressions and 3D head pose changes.

Graphical abstractFigure optionsDownload full-size imageDownload high-quality image (203 K)Download as PowerPoint slideHighlights► In this paper we look at the use of automatic facial expression detection as a means of monitoring patient care. ► To do this, we have released the UNBC-McMaster Shoulder Pain Archive which is freely available for academic use. ► The dataset includes: 200 sequences across 25 subjects, of fully coded spontaneous facial expressions with 66-point AAM tracked facial feature landmarks. ► We provide baseline results for both frame-by-frame and sequence-level experiments. ► We also look at using 3D head pose parameters for pain detection.