A novel pain sentiment detection system utilizing a PainCapsule model and textual facial patterns

Patient sentiment analysis establishes an intricate relationship between pain management and sentiment analysis in delivering high-quality medical care. This work presents an efficient pain sentiment recognition system within a smart healthcare framework designed to assess patients’ pain levels by analyzing their facial expressions. The proposed system is implemented in four distinct phases. First, facial regions are detected using efficient face-detection techniques. In the second phase, the extracted facial regions undergo feature computation using advancements in deep learning techniques, including end-to-end and pre-trained convolutional neural networks ($CNN$) to capture complex and discriminative facial features associated with pain emotions. In the third phase, a novel $PainCapsule$ model is introduced, which evaluates pain intensity by analyzing both macro- and microfacial expressions. This phase also employs attention networks, feature tuning, and transfer learning techniques to optimize the system’s performance. Finally, in the fourth phase, score fusion techniques are applied to the deep pain recognition models to enhance accuracy and robustness further. The system’s effectiveness is rigorously evaluated using two benchmark video datasets: the BioVid Heat Pain Dataset and the Multimodal Intensity Pain (MIntPAIN) database. Extensive experiments and comparative analysis with existing state-of-the-art methods reveal that the proposed system achieves an F1-score of 65.51% for BioVid and 58.31% for MIntPAIN datasets, outperforming other pain recognition systems, demonstrating its potential to advance pain sentiment recognition within smart healthcare frameworks.