Pain communication varies significantly among individuals, some are highly expressive, while others demonstrate stoic restraint and offer minimal verbal indication of discomfort. Substantial progress has been made in identifying behavioral indicators of pain. A growing body of literature highlights measurable indices of pain through facial expressions, vocalizations, body movements, as well as physiological and neural responses. To enhance the reliability of pain monitoring, automated pain assessment has emerged as a promising approach. Although available datasets remain limited, they are steadily increasing, helping to drive research forward. Despite notable progress, this field is still in its early stages. The 5th edition of the AAP workshop continues to seek to highlight current research and foster interdisciplinary collaboration and discussion to accelerate progress in this important area.

"Multi-modal AI-Based Pain Detection in Intermediate Care Patients in the Postoperative Phase" is an interdisciplinary research work that operates in the domain of automated pain detection. It aims to improve previous work, based on pain databases like BioVid and UNBC shoulder pain, as well as AI-based approaches using computer vision and signal processing to analyze available modalities. Thus, we present our basic research idea on how to improve automatic pain detection in three major steps. The first step focuses on collecting pain data from postoperative patients in intermediate care stations (IMC). In addition, patients who are not fully oriented should be included in a separate data collection as a second focus group. Then, improvements on the state-of-the-art models should not only advance general pain detection, but also help bridge the gap to the real-world setting of the IMC data. Improvements include transferability analysis, feature selection evaluation, and balancing of data distribution to deliver better classification performance. In a last step, we aim to test, verify and evaluate the classification performance on the IMC data with the support of medical practitioners.

Pain is a complex phenomenon, involving sensory and emotional experience, that is often poorly understood, especially in infants, anesthetized patients, and others who cannot speak. Technology supporting pain assessment has the potential to help reduce suffering; however, advances are needed before it can be adopted clinically. This survey paper assesses the state of the art and provides guidance for researchers to help make such advances. First, we overview pain’s biological mechanisms, physiological and behavioral responses, emotional components, as well as assessment methods commonly used in the clinic. Next, we discuss the challenges hampering the development and validation of pain recognition technology, and we survey existing datasets together with evaluation methods. We then present an overview of all automated pain recognition publications indexed in the Web of Science as well as from the proceedings of the major conferences on biomedical informatics and artificial intelligence, to provide understanding of the current advances that have been made. We highlight progress in both non-contact and contact-based approaches, tools using face, voice, physiology, and multi-modal information, the importance of context, and discuss challenges that exist, including identification of ground truth. Finally, we identify underexplored areas such as chronic pain and connections to treatments, and describe promising opportunities for continued advances.

No abstract available.

The subjective nature of pain makes it a very challenging phenomenon to assess. Most of the current pain assessment approaches rely on an individual’s ability to recognise and report an observed pain episode. However, pain perception and expression are affected by numerous factors ranging from personality traits to physical and psychological health state. Hence, several approaches have been proposed for the automatic recognition of pain intensity, based on measurable physiological and audiovisual parameters. In the current paper, an assessment of several fusion architectures for the development of a multi-modal pain intensity classification system is performed. The contribution of the presented work is two-fold: (1) 3 distinctive modalities consisting of audio, video and physiological channels are assessed and combined for the classification of several levels of pain elicitation. (2) An extensive assessment of several fusion strategies is carried out in order to design a classification architecture that improves the performance of the pain recognition system. The assessment is based on theSenseEmotion Databaseand experimental validation demonstrates the relevance of the multi-modal classification approach, which achieves classification rates of respectively$83.39\%$,$59.53\%$and$43.89\%$in a 2-class, 3-class and 4-class pain intensity classification task.

Pain is a primary symptom in medicine, and accurate assessment is needed for proper treatment. However, today's pain assessment methods are not sufficiently valid and reliable in many cases. Automatic recognition systems may contribute to overcome this problem by facilitating objective and continuous assessment. In this article we propose a novel feature set for describing facial actions and their dynamics, which we call facial activity descriptors. We apply them to detect pain and estimate the pain intensity. The proposed method outperforms previous state-of-the-art approaches in sequence-level pain classification on both, the BioVid Heat Pain and the UNBC-McMaster Shoulder Pain Expression database. We further discuss major challenges of pain recognition research, benefits of temporal integration, and shortcomings of widely used frame-based pain intensity ground truth.

Non-contact measurement of the heart rate is more comfortable than classical methods and can facilitate new applications. However, current approaches are very susceptible to motion. Aiming at overcoming this limitation, we propose a new, more robust approach to estimate the heart rate from a videotaped face. It features non-planar motion compensation, fusion of multiple ROI signals, and a RANSAC-like time-domain heart rate estimation algorithm. In experiments with a comprehensive pain recognition dataset we show that our approach outperforms previous methods in the presence of spontaneous head movement and facial expression.

How much does it hurt? Accurate assessment of pain is very important for selecting the right treatment, however current methods are not sufficiently valid and reliable in many cases. Automatic pain monitoring may help by providing an objective and continuous assessment. In this paper we propose an automatic pain recognition system combining information from video and biomedical signals, namely facial expression, head movement, galvanic skin response, electromyography and electrocardiogram. Using the BioVid Heat Pain Database, the system is evaluated in the task of pain detection showing significant improvement over the current state of the art. Further, we discuss the relevance of the modalities and compare person-specific and generic classification models.

Pain is what the patient says it is. But what about these who cannot utter? Automatic pain monitoring opens up prospects for better treatment, but accurate assessment of pain is challenging due to the subjective nature of pain. To facilitate advances, we contribute a new dataset, the BioVid Heat Pain Database which contains videos and physiological data of 90 persons subjected to well-defined pain stimuli of 4 intensities. We propose a fully automatic recognition system utilizing facial expression, head pose information and their dynamics. The approach is evaluated with the task of pain detection on the new dataset, also outlining open challenges for pain monitoring in general. Additionally, we analyze the relevance of head pose information for pain recognition and compare person-specific and general classification models.

The goal of automatic biopsychological emotion recognition of companion technologies is to ensure reliable and valid classification rates. In this paper, emotional states were induced via a Wizard-of-Oz mental trainer scenario, which is based on the valence-arousal-dominance model. In most experiments, classification algorithms are tested via leave-out cross-validation of one situation. These studies often show very high classification rates, which are comparable with those in our experiment (92.6%). However, in order to guarantee robust emotion recognition based on biopsychological data, measurements have to be taken across several situations with the goal of selecting stable features for individual emotional states. For this purpose, our mental trainer experiment was conducted twice for each subject with a 10-min break between the two rounds. It is shown that there are robust psychobiological features that can be used for classification (70.1%) in both rounds. However, these are not the same as those that were found via feature selection performed only on the first round (classification: 53.0%).

A critical task in Affective Computing is the reliable assessment of emotional states. The two most prominent approaches to classify emotions are categorical concepts of discrete emotions (e.g. OCC) and dimensional models typically using the pleasure - arousal - dominance space (PAD). In current research and applications, however, there is little overlap between these two concepts. A mapping of discrete categories into the dimensional space would offer new possibilities to model the emotional states of users and artificial agents, though. We hence let N=70 healthy subjects place the labels of discrete OCC emotions into PAD space according to their subjective knowledge with a simple visual tool. There was a high inter-subject consistency regarding the positioning of OCC emotions for the dimension of pleasure. However, arousal and dominance ratings showed considerably greater variance. We conclude that global and reliable mappings of OCC emotions into the PAD space can best be provided for the pleasure dimension. The exact positioning of discrete emotions regarding arousal and dominance can only be gained by individual calibration of a given user in a strict within-subject approach.

In this paper we present a study on language use of people with different verbal intelligence. We asked test persons of different ages and educational background to describe the same event. Verbal intelligence was measured using the Hamburg Wechsler Intelligence Test for Adults. The transcribed monologues were analyzed using the DeLite readability checker and different linguistic features used for readability calculations were extracted. The test persons were then divided into two groups according to the results of the intelligence test using the SEM algorithm. For each group the averaged values of the features extracted from the monologues were compared using a one-way analysis of variance.