Personalized Automatic Estimation of Self-reported Pain Intensity from Facial Expressions

Pain is a personal, subjective experience that is commonly evaluated through visual analog scales (VAS). While this is often convenient and useful, automatic pain detection systems can reduce pain score acquisition efforts in large-scale studies by estimating it directly from the participants' facial expressions. In this paper, we propose a novel two-stage learning approach for VAS estimation: first, our algorithm employs Recurrent Neural Networks (RNNs) to automatically estimate Prkachin and Solomon Pain Intensity (PSPI) levels from face images. The estimated scores are then fed into the personalized Hidden Conditional Random Fields (HCRFs), used to estimate the VAS, provided by each person. Personalization of the model is performed using a newly introduced facial expressiveness score, unique for each person. To the best of our knowledge, this is the first approach to automatically estimate VAS from face images. We show the benefits of the proposed personalized over traditional non-personalized approach on a benchmark dataset for pain analysis from face images.Comment: Computer Vision and Pattern Recognition Conference, The 1st International Workshop on Deep Affective Learning and Context Modelin

Facial expression of pain: an evolutionary account.

This paper proposes that human expression of pain in the presence or absence of caregivers, and the detection of pain by observers, arises from evolved propensities. The function of pain is to demand attention and prioritise escape, recovery, and healing; where others can help achieve these goals, effective communication of pain is required. Evidence is reviewed of a distinct and specific facial expression of pain from infancy to old age, consistent across stimuli, and recognizable as pain by observers. Voluntary control over amplitude is incomplete, and observers can better detect pain that the individual attempts to suppress rather than amplify or simulate. In many clinical and experimental settings, the facial expression of pain is incorporated with verbal and nonverbal vocal activity, posture, and movement in an overall category of pain behaviour. This is assumed by clinicians to be under operant control of social contingencies such as sympathy, caregiving, and practical help; thus, strong facial expression is presumed to constitute and attempt to manipulate these contingencies by amplification of the normal expression. Operant formulations support skepticism about the presence or extent of pain, judgments of malingering, and sometimes the withholding of caregiving and help. To the extent that pain expression is influenced by environmental contingencies, however, "amplification" could equally plausibly constitute the release of suppression according to evolved contingent propensities that guide behaviour. Pain has been largely neglected in the evolutionary literature and the literature on expression of emotion, but an evolutionary account can generate improved assessment of pain and reactions to it

Objectively measuring pain using facial expression: is the technology finally ready?

Currently, clinicians observe pain-related behaviors and use patient self-report measures in order to determine pain severity. This paper reviews the evidence when facial expression is used as a measure of pain. We review the literature reporting the relevance of facial expression as a diagnostic measure, which facial movements are indicative of pain, and whether such movements can be reliably used to measure pain. We conclude that although the technology for objective pain measurement is not yet ready for use in clinical settings, the potential benefits to patients in improved pain management, combined with the advances being made in sensor technology and artificial intelligence, provide opportunities for research and innovation