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The current study examined youths' and their parents' perceptions concerning participation in an investigation of spontaneous and induced pain during recovery from laparoscopic appendectomy. Youth (age range 5-17 years) and their parents independently completed surveys about their study participation. On a 0 (very negative) -to-10 (very positive) scale, both parents 9.4(1.3) [mean(SD)] and youth 7.9(2.4) rated their experience as positive. Among youth, experience ratings did not differ by pain severity and survey responses did not differ by age. Most youth (83%) reported they would tell another youth to participate. Ethical issues regarding instigation of pain in youth for research purposes are examined.

Objective pain assessment is required for appropriate pain management in the clinical setting. However, clinical gold standard pain assessment is based on subjective methods. Automated pain detection from physiological data may provide important objective information to better standardize pain assessment. Specifically, electrodermal activity (EDA) can identify features of stress and anxiety induced by varying pain levels. However, notable variability in EDA measurement exists and research to date has demonstrated sensitivity but lack of specificity in pain assessment. In this paper, we use timescale decomposition (TSD) to extract salient features from EDA signals to identify an accurate and automated EDA pain detection algorithm to sensitively and specifically distinguish pain from no-pain conditions.

Accurately determining pain levels in children is difficult, even for trained professionals and parents. Facial activity provides sensitive and specific information about pain, and computer vision algorithms have been developed to automatically detect Facial Action Units (AUs) defined by the Facial Action Coding System (FACS). Our prior work utilized information from computer vision, i.e., automatically detected facial AUs, to develop classifiers to distinguish between pain and no-pain conditions. However, application of pain/no-pain classifiers based on automated AU codings across different environmental domains results in diminished performance. In contrast, classifiers based on manually coded AUs demonstrate reduced environmentally-based variability in performance. In this paper, we train a machine learning model to recognize pain using AUs coded by a computer vision system embedded in a software package called iMotions. We also study the relationship between iMotions (automatically) and human (manually) coded AUs. We find that AUs coded automatically are different from those coded by a human trained in the FACS system, and that the human coder is less sensitive to environmental changes. To improve classification performance in the current work, we applied transfer learning by training another machine learning model to map automated AU codings to a subspace of manual AU codings to enable more robust pain recognition performance when only automatically coded AUs are available for the test data. With this transfer learning method, we improved the Area Under the ROC Curve (AUC) on independent data from new participants in our target domain from 0.67 to 0.72.

Objective To examine whether caregiver judgments of infant pain would vary systematically with different infant caregiver groups and infant age. Methods A total of 123 caregivers (41 parents, 41 in patient nurses, 41 pediatricians) viewed videotapes of the vigorous behavioral responses of healthy infants (aged 2, 4, 6, 12, and 18 months) to a routine immunization injection and provided ratings of both the affective distress and pain intensity observed. Results A principal components analysis of affective and intensity ratings yielded a weighted pain summary score for each injection event. Older infants were attributed significantly more pain than younger infants, even though the vigor of the behavioral reactions was experimentally controlled across age groups. A profile analysis contrasting observer groups indicated that pediatricians attributed significantly lower levels of pain than parents, while nurses were intermediate to the other groups, not significantly differing from either group. These systematic differences in judgments were consistent across infant age groups. Conclusions The findings reveal systematic sources of significant variations in observer judgments of infant pain. Despite an absence of differences in the behavioral reactions of the children, both the type of caregiver and their knowledge of the child's age systematically influenced attributions of pain to infants. This work suggests the important role of caregiver role variation and perceived developmental maturity as determinants of infant pain judgments and highlights potential areas of difficulty in controlling the unnecessary suffering of infants. [ABSTRACT FROM AUTHOR]

Objective: To examine whether children with developmental delays respond to painful events differently than nondelayed children.Methods: Sixty families participated. Children between the ages of 2 and 6 years were observed at daycare centers while engaged in usual daily activities, such as free play. Spontaneous painful incidents and the child's responses were recorded using an observational measure (Dalhousie Everyday Pain Scale) designed to capture pain behavior.Results: Children with developmental delays (n = 24) displayed a less intense distress response to an equivocal pain event than nondelayed children (n = 36). Children with developmental delays were more likely to display no reaction following a pain event, whereas children without delays cried more often. Further, children with developmental delays engaged in fewer help-seeking behaviors and were less likely to display a social response following a pain event than nondelayed children.Conclusions: Children with developmental delays appear to react in a different manner to pain events than nondelayed children do; we discuss a possible socio-communicative deficit. [ABSTRACT FROM AUTHOR]

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.

Accurately determining pain levels in children is difficult, even for trained professionals and parents. Facial activity provides sensitive and specific information about pain, and computer vision algorithms have been developed to automatically detect Facial Action Units (AUs) defined by the Facial Action Coding System (FACS). Our prior work utilized information from computer vision, i.e., automatically detected facial AUs, to develop classifiers to distinguish between pain and no-pain conditions. However, application of pain/no-pain classifiers based on automated AU codings across different environmental domains results in diminished performance. In contrast, classifiers based on manually coded AUs demonstrate reduced environmentally-based variability in performance. In this paper, we train a machine learning model to recognize pain using AUs coded by a computer vision system embedded in a software package called iMotions. We also study the relationship between iMotions (automatically) and human (manually) coded AUs. We find that AUs coded automatically are different from those coded by a human trained in the FACS system, and that the human coder is less sensitive to environmental changes. To improve classification performance in the current work, we applied transfer learning by training another machine learning model to map automated AU codings to a subspace of manual AU codings to enable more robust pain recognition performance when only automatically coded AUs are available for the test data. With this transfer learning method, we improved the Area Under the ROC Curve (AUC) on independent data from new participants in our target domain from 0.67 to 0.72.