Infants' Pain Recognition Based on Facial Expression: Dynamic Hybrid Descriptions

Abstract

SUMMARYThe 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%). key words: infants pain assessment, temporal geometric descriptor, LBP-TOP, decision fusion, video analysis

Automatic Infants’ Pain Assessment by Dynamic Facial Representation: Effects of Profile View, Gestational Age, Gender, and Race

“…Multiple dynamic facial features provide sufficient information for infant pain assessment and are tolerant to missing data caused by hospitalization environments, such as occlusions. More details of the comparison of multiple dynamic facial features, including feature fusion and decision fusion, are stated in our previous study [ 48 ]. Based on these observations, we apply the three-feature decision fusion in subsequent sections for further analysis.…”

“…Hybrid pain facial activity representation, including facial geometric features and texture features, is studied for pain facial expression recognition. The temporal pain-related facial activity representation is the same as in our preliminary work [ 48 ]. The dimensionality of hybrid facial features is reduced by manifold learning-based Supervised Locality Preserving Projections (SLPP) method, and SVM classifier is utilized for pain recognition.…”

Implementasi Convolutional Neural Network Untuk Deteksi Nyeri Bayi Melalui Citra Wajah Dengan YOLO

“…Sensitifitas yang cukup tinggi sebagai alat skrening nyeri namun memiliki spesifitas yang sedang sebagai alat diagnostik nyeri pada anak [14], lalu terdapat juga pengembangan dengan menggunakan wajah bayi, suara. Di mana pergerakan diambil dengan menggunakan kamera go-pro lalu dengan menggunakan metode machine learning, svm classifier, dengan output untuk menentukan pain atau no pain [20]. Serta terdapat pula dengan cara mengklasifikasikan respon wajah pada bayi menggunakan CNN ketika mengalami suatu rasa nyeri [21].…”

Multi-Channel Neural Network for Assessing Neonatal Pain from Videos

“…Examples of these cues include facial expression, body movement, crying sound, and vital sign readings. Among all pain cues, facial expression is considered the most validated, prominent, and pain-specific cue [5], [6], [7].…”