A Multi-center Clinical Trial for Camera-based Infant Sleep and Awake Detection in Neonatal Intensive Care Unit

Infants need adequate sleep to develop their brain and cardiovascular systems, especially for preterm infants in the Neonatal Intensive Care Unit. Camera-based infant monitoring is an emerging direction of research in video health monitoring. Intuitively, camera can easily identify the sleep-awake stage of infants by detecting the state of eyes, e.g. closed or opening. Thus in this paper, we propose to explore the unique advantage of camera-based facial analysis for sleep-awake detection, as a fundamental step toward infant sleep monitoring. A multi-center clinical trial was conducted to collect infant videos for investigating the feasibility of our proposal. A benchmark including four machine learning methods of SVM, KNN, MLP, and CNN (ResNet18) was set up to classify the sleep/awake stage of infants. To alleviate the overfitting issue caused by over-sampling of a sleeping infant, we propose to integrate ResNet18 with the contrastive learning strategy to strengthen the consistency of facial features learned from different infants. The clinical evaluation shows that all benchmarked methods obtained an accuracy above 75% while the proposed method achieved the best accuracy of 86%. This invokes further explorations of using facial/eye features of infants for sleep-awake staging, towards intelligent contactless sleep analysis of infants in combination with camera-based vital signs monitoring.