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The Automatic Pain Assessment (APA) relies on the exploitation of objective methods to evaluate the severity of pain and other pain-related characteristics. Facial expressions are the most investigated pain behavior features for APA. We constructed a binary classifier model for discriminating between the absence and presence of pain through video analysis. A brief interview lasting approximately two-minute was conducted with cancer patients, and video recordings were taken during the session. The Delaware Pain Database and UNBC-McMaster Shoulder Pain dataset were used for training. A set of 17 Action Units (AUs) was adopted. For each image, the OpenFace toolkit was used to extract the considered AUs. The collected data were grouped and split into train and test sets: 80 % of the data was used as a training set and the remaining 20 % as the validation set. For continuous estimation, the entire patient video with frame prediction values of 0 (no pain) or 1 (pain), was imported into an annotator (ELAN 6.4). The developed Neural Network classifier consists of two dense layers. The first layer contains 17 nodes associated with the facial AUs extracted by OpenFace for each image. The output layer is a classification label of "pain" (1) or "no pain" (0). The classifier obtained an accuracy of ∼94 % after about 400 training epochs. The Area Under the ROC curve (AUROC) value was approximately 0.98. This study demonstrated that the use of a binary classifier model developed from selected AUs can be an effective tool for evaluating cancer pain. The implementation of an APA classifier can be useful for detecting potential pain fluctuations. In the context of APA research, further investigations are necessary to refine the process and particularly to combine this data with multi-parameter analyses such as speech analysis, text analysis, and data obtained from physiological parameters. [ABSTRACT FROM AUTHOR]

Perception and expression of pain in cancer patients are influenced by distress levels, tumor type and progression, and the underlying pathophysiology of pain. Relying on traditional pain assessment tools can present limitations due to the highly subjective and multifaceted nature of the symptoms. In this scenario, objective pain assessment is an open research challenge. This work introduces a framework for automatic pain assessment. The proposed method is based on a wearable biosignal platform to extract quantitative indicators of the patient pain experience, evaluated through a self-assessment report. Two preliminary case studies focused on the simultaneous acquisition of electrocardiography (ECG), electrodermal activity (EDA), and accelerometer signals are illustrated and discussed. The results demonstrate the feasibility of the approach, highlighting the potential of EDA in capturing skin conductance responses (SCR) related to pain events in chronic cancer pain. A weak correlation (R = 0.2) is found between SCR parameters and the standard deviation of the interbeat interval series (SDRR), selected as the Heart Rate Variability index. A statistically significant (p < 0.001) increase in both EDA signal and SDRR is detected in movement with respect to rest conditions (assessed by means of the accelerometer signals) in the case of motion-associated cancer pain, thus reflecting the relationship between motor dynamics, which trigger painful responses, and the subsequent activation of the autonomous nervous system. With the objective of integrating parameters obtained from biosignals to establish pain signatures within different clinical scenarios, the proposed framework proves to be a promising research approach to define pain signatures in different clinical contexts. [ABSTRACT FROM AUTHOR]

The effective assessment of cancer pain requires a meticulous analysis of all the components that shape the painful experience collectively. Implementing Automatic Pain Assessment (APA) methods and computational analytical approaches, with a specific focus on emotional content, can facilitate a thorough characterization of pain. The proposed approach moves towards the use of automatic emotion recognition from speech recordings alongside a model we previously developed to examine facial expressions of pain. For training and validation, we adopted the EMOVO dataset, which simulates six emotional states (the Big Six). A Neural Network, consisting of a Multi-Layered Perceptron, was trained on 181 prosodic features to classify emotions. For testing, we used a dataset of interviews collected from cancer patients and selected two case studies. Speech annotation and continuous facial expression analysis (resulting in pain/no pain classifications) were carried out using Eudico Linguistic Annotator (ELAN) version 6.7. The model for emotion analysis achieved 84% accuracy, with encouraging precision, recall, and F1-score metrics across all classes. The preliminary results suggest the potential use of artificial intelligence (AI) strategies for continuous estimation of emotional states from video recordings, unveiling predominant emotional states, and providing the ability to corroborate the corresponding pain assessment. Despite limitations, the proposed AI framework exhibits potential for holistic and realtime pain assessment, paving the way for personalized pain management strategies in oncological settings. Clinical Trial registration: NCT04726228. [ABSTRACT FROM AUTHOR]