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Frequent pain monitoring in intensive care unit (ICU) settings has been shown to have achieved improvement in patient outcomes. Specifically, the nursing staff is required to assess the pain of the patients approximately every four hours, and make necessary pain medication adjustments. Shortage and overburdening of nursing staff is well acknowledged in many hospital settings worldwide. In this paper, we present a preliminary study on automatic recognition of facial expression of pain in ICU settings. There has been considerable work on computer based pain recognition for settings beyond the ICU, wherein, usually the entire face of the person is visible without occlusions. The ICU setting, however, brings along unique challenges for recognizing facial expression of pain – the patient’s face is often covered by a respirator mask, the face is partially occluded by accessories attached to the respirator, and the patient is occasionally in a transient state of consciousness. In this paper we investigate the use of computer vision techniques for recognizing pain from partially visible faces. To establish a proof-of-concept, we simulate the occlusions most likely to happen in an ICU setting by masking out the nose, cheeks, and mouth regions, and use only a narrow band around the eyes of the patient as an input to our method. We generate these simulated occlusions using data from the UNBC-McMaster pain expression archive −a previously used dataset for pain expression recognition based on fully visible faces. We investigate multiple feature representations by extracting features only on the basis of the eye-region and train classifiers to detect the presence of pain using leave-one-person-out cross validation. Our results suggest a potential viability of automatic pain monitoring in the ICU settings involving face occlusions.

Pain is a transient physical reaction that exhibits on human faces. Automatic pain intensity estimation is of great importance in clinical and health-care applications. Pain expression is identified by a set of deformations of facial features. Hence, features are essential for pain estimation. In this paper, we propose a novel method that encodes low-level descriptors and powerful high-level deep features by a weighting process, to form an efficient representation of facial images. To obtain a powerful and compact low-level representation, we explore the way of using second-order pooling over the local descriptors. Instead of direct concatenation, we develop an efficient fusion approach that unites the low-level local descriptors and the high-level deep features. To the best of our knowledge, this is the first approach that incorporates the low-level local statistics together with the high-level deep features in pain intensity estimation. Experiments are evaluated on the benchmark databases of pain. The results demonstrate that the proposed low-to-high-level representation outperforms other methods and achieves promising results.

Pain is one of the most common and distressing symptoms reported by emergency room patients. Valid and reliable assessment of pain is essential for both clinical trials and effective pain management. A major limitation of automatic pain assessment by the facial expression is the lack of clinically validated data. This work aims at collecting a pain expression database for pain intensity analysis in clinical settings. The database includes 140 color video sequences, 140 multi-sensor sequences obtained by the Kinect 2, patients' sequence-level self-report, Cyclooxygenases (COXs) level, and inducible nitric oxide synthase (iNOS) level in the blood samples. The database also includes head poses and derived facial landmarks from the 2D video. The relationships of their self-report, Cyclooxygenases (COXs), inducible nitric oxide synthase (iNOS), head pose and facial expression are analyzed. The correlation between the clinical and non-clinical pain facial expressions have been evaluated as well.

To develop automatic pain monitoring systems, we need a deep understanding of pain expression and its influencing factors and we need datasets with high-quality labels. This work analyzes the variation of facial activity with pain stimulus intensity and among subjects. We propose two distinct methods to assess facial expressiveness and apply them on the BioVid Heat Pain Database. Experimental results show that facial response is rare during low intensity pain stimulation and that the proposed measures can successfully identify highly expressive individuals, for whom pain stimuli can be classified reliably, and non-expressive individuals, who may have felt less pain than intended and encoded in labels.

Developing a vision-based efficient and automatic pain intensity measurement system requires the understanding of the relationship between self-reported pain intensity and pain expression in the facial videos. In this paper, we first demonstrate how pain expression in facial video frames may not match with the self-reported score. This is because the pain and non-pain frames are not always visually distinctive; though the self-report tells different story of having pain and non-pain status. On the other hand previous studies reported that general facial expressions can be used as biometrics. Thus, in this paper we investigated the relevance of pain expression from facial video to be used as a biometric or soft-biometric trait. In order to do that, we employed a biometric person recognition scenario by using features obtained from the pain expression pattern found in the temporal axis of subjects’ videos. The results confirmed that the pain expression patterns have distinctive features between the subjects of the UNBC McMaster shoulder pain database. We concluded that as the pain expression patterns have subjective features as a biometric, this can also cause the difference between self-reported pain level and the visually observed pain intensity level.

The presence of imbalanced data distribution is evident in most real-life datasets. The problem of learning from imbalanced data is a challenging task due to presence of underrepresented data and severe class distribution skews. In this paper we recognizes 15 different levels of shoulder pain intensities based on facial expressions using UNBC-McMaster Shoulder Pain Expression Archive database which has highly imbalanced data distribution among its classes. A 22 dimensional geometric features are extracted from detected facial landmarks. The feature set is balanced using Synthetic Minority Oversampling Technique (SMOTE) and also using Adaptive Synthetic Sampling (ADASYN). A recognition technique is developed using Gaussian Mixture Regression (GMR) to recognize the fifteen different intensity levels. Comprehensive experiments with various settings show that the proposed pain intensity recognition system using SMOTE and GMR yields stable and promising recognition results.

Timely and accurate information about patients' symptoms is important for clinical decision making such as adjustment of medication. Due to the limitations of self-reported symptom such as pain, we investigated whether facial images can be used for detecting pain level accurately using existing algorithms and infrastructure for cancer patients. For low cost and better pain management solution, we present a smart phone based system for pain expression recognition from facial images. To the best of our knowledge, this is the first study for mobile based chronic pain intensity detection. The proposed algorithms classify faces, represented as a weighted combination of Eigenfaces, using an angular distance, and support vector machines (SVMs). A pain score was assigned to each image by the subject. The study was done in two phases. In the first phase, data were collected as a part of a six month long longitudinal study in Bangladesh. In the second phase, pain images were collected for a cross-sectional study in three different countries: Bangladesh, Nepal and the United States. The study shows that a personalized model for pain assessment performs better for automatic pain assessment and the training set should contain varying levels of pain representing the application scenario.

Automatic pain recognition can improve medical treatment, especially when the patient is not able to utter on his pain experience. Facial expressions with their intensities and dynamics contain valuable information for recognising pain. We propose a concept for distinguishing facial expressions of pain from others and assessing the pain expression intensity. It is based on a Support Vector Machine (SVM) classifier and a function model for intensity rating. The intensity model is trained using Comparative Learning, a new technique that simplifies labelling of the data. Using a database of 3D posed pain sequences we show the suitability of the concept to recognise pain expressions, distinguish different intensities and spot even slight intensity changes in its temporal context.

A major factor hindering the deployment of a fully functional automatic facial expression detection system is the lack of representative data. A solution to this is to narrow the context of the target application, so enough data is available to build robust models so high performance can be gained. Automatic pain detection from a patient's face represents one such application. To facilitate this work, researchers at McMaster University and University of Northern British Columbia captured video of participant's faces (who were suffering from shoulder pain) while they were performing a series of active and passive range-of-motion tests to their affected and unaffected limbs on two separate occasions. 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. This database is called the UNBC-McMaster Shoulder Pain Expression Archive Database. To promote and facilitate research into pain and augment current datasets, we have publicly made available a portion of this database which includes: 1) 200 video sequences containing spontaneous facial expressions, 2) 48,398 FACS coded frames, 3) associated pain frame-by-frame scores and sequence-level self-report and observer measures, and 4) 66-point AAM landmarks. This paper documents this data distribution in addition to describing baseline results of our AAM/SVM system. This data will be available for distribution in March 2011.

Pain assessment is of high priority in the clinical setting. Facial pain scales (FPSs) are pain assessment tools generally used with school-aged children. The implicit theoretical bases for the success of FPSs have seldom been explored. Explanations why and how FPSs work (or do not work) have not been addressed. We support the existence of a universal pain expression - the primal face of pain (PFP), which is present at birth, evolved in nature, and modulated through sociocultural factors. We propose it to be key in understanding the applicability of FPSs. We present here the design of a computer-assisted descriptive study that will observe, quantify and model the PFP as present in newborns. Measurement of the PFP will lead to exploration of the theoretical consequences of its existence, particularly as related to pediatric pain assessment and the valid use of FPSs. Further, this work can lay a foundation for the development of a new generation of FPSs.