The accurate assessment of infants' pain is important for understanding their medical conditions and developing suitable treatment. Pediatric studies reported that the inadequate treatment of infants' pain might cause various neuroanatomical and psychological problems. The fact that infants can not communicate verbally motivates increasing interests to develop automatic pain assessment system that provides continuous and accurate pain assessment. In this paper, we propose a new set of pain facial activity features to describe the infants' facial expression of pain. Both dynamic facial texture feature and dynamic geometric feature are extracted from video sequences and utilized to classify facial expression of infants as pain or no pain. For the dynamic analysis of facial expression, we construct spatiotemporal domain representation for texture features and time series representation (i.e. time series of frame-level features) for geometric features. Multiple facial features are combined through both feature fusion and decision fusion schemes to evaluate their effectiveness in infants' pain assessment. Experiments are conducted on the video acquired from NICU infants, and the best accuracy of the proposed pain assessment approaches is 95.6%. Moreover, we find that although decision fusion does not perform better than that of feature fusion, the False Negative Rate of decision fusion (6.2%) is much lower than that of feature fusion (25%).

Pain is an essential physiological phenomenon of human beings. Accurate assessment of pain is important to develop proper treatment. Although self-report method is the gold standard in pain assessment, it is not applicable to individuals with communicative impairment. Non-verbal pain indicators such as pain related facial expressions and changes in physiological parameters could provide valuable insights for pain assessment. In this paper, we propose a multimodal-based Stream Integrated Neural Network with Different Frame Rates (SINN) that combines facial expression and biomedical signals for automatic pain assessment. The main contributions of this research are threefold. (1) There are four-stream inputs of the SINN for facial expression feature extraction. The variant facial features are integrated with biomedical features, and the joint features are utilized for pain assessment. (2) The dynamic facial features are learned in both implicit and explicit manners to better represent the facial changes that occur during pain experience. (3) Multiple modalities are utilized to identify various pain states, including facial expression and biomedical signals. The experiments are conducted on publicly available pain datasets, and the performance is compared with several deep learning models. The experimental results illustrate the superiority of the proposed model, and it achieves the highest accuracy of 68.2%, which is up to 5% higher than the basic deep learning models on pain assessment with binary classification.