  Current day pain assessment methods rely on patient self-report or by an
observer like the Intensive Care Unit (ICU) nurses. Patient self-report is
subjective to the individual and suffers due to poor recall. Pain assessment by
manual observation is limited by the number of administrations per day and
staff workload. Previous studies showed the feasibility of automatic pain
assessment by detecting Facial Action Units (AUs). Pain is observed to be
associated with certain facial action units (AUs). This method of pain
assessment can overcome the pitfalls of present-day pain assessment techniques.
All the previous studies are limited to controlled environment data. In this
study, we evaluated the performance of OpenFace an open-source facial behavior
analysis tool and AU R-CNN on the real-world ICU data. Presence of assisted
breathing devices, variable lighting of ICUs, patient orientation with respect
to camera significantly affected the performance of the models, although these
showed the state-of-the-art results in facial behavior analysis tasks. In this
study, we show the need for automated pain assessment system which is trained
on real-world ICU data for clinically acceptable pain assessment system.
