Machine learning methods on BioVid heat pain database for pain intensity estimation

Pain assessment is a critical aspect of medical practice, directly influencing patient treatment and quality of life. Traditional pain evaluation methods, such as the Numerical Rating Scale (NRS), Visual Analog Scale (VAS), and Verbal Rating Scale (VRS), are subjective and often unreliable, especially for non-verbal, unconscious, or cognitively impaired patients. This study introduces an objective pain measurement model using advanced machine learning techniques, specifically, Convolutional Neural Networks (CNNs), and Vision Transformers (ViTs), to analyze facial expressions. We compared the performance of CNNs, VGG16, Convolutional Vision Transformer (CvT), and MobileViT, in classifying pain intensity based on facial images captured during peak pain and no-pain moments. The models were trained and evaluated on the BioVid Heat Pain Database, which comprises facial recordings of 87 participants experiencing varying pain intensities. The comparative analysis of the CNN, VGG16, CvT, and MobileViT50 models reveal distinct differences in their performance metrics. Among the four models, the CNN model achieved the highest average accuracy at 0.71, demonstrating better performance in correctly classifying both pain and no pain images. The CvT model followed closely with an average accuracy of 0.69, indicating that it also performed well, although slightly less effectively than CNN. MobileViT50, with an accuracy of 0.60, and VGG16, with 0.56, performed significantly lower, suggesting that these models struggled more with accurately classifying the data. These results highlight the potential of automated pain assessment technologies to provide consistent and objective evaluations, which can be particularly beneficial in clinical environments for non-communicative patients. Future research will explore the integration of multimodal data to further enhance the robustness of pain detection systems.