| Title | Preterm Newborn Presence Detection in Incubator and Open Bed Using Deep Transfer Learning. |
| Author(s) | Weber R, Cabon S, Simon A, Poree F, Carrault G. |
| Source | IEEE J Biomed Health Inform., Vol. 25, No. 5, Pages 1419-1428 |
| DOI | doi: 10.1109/JBHI.2021.3062617. Epub 2021 |
| Publication Date | 5/1/2021 |
| Abstract | Video-based motion analysis recently appeared to be a promising approach in neonatal intensive care units for monitoring the state of preterm newborns since it is contact-less and noninvasive. However it is important to remove periods when the newborn is absent or an adult is present from the analysis. In this paper, we propose a method for automatic detection of preterm newborn presence in incubator and open bed. We learn a specific model for each bed type as the camera placement differs a lot and the encountered situations are different between both. We break the problem down into two binary classifications based on deep transfer learning that are fused afterwards: newborn presence detection on the one hand and adult presence detection on the other hand. Moreover, we adopt a strategy of decision intervals fusion in order to take advantage of temporal consistency. We test three deep neural network that were pre-trained on ImageNet: VGG16, MobileNetV2 and InceptionV3. Two classifiers are compared: support vector machine and a small neural network. Our experiments are conducted on a database of 120 newborns. The whole method is evaluated on a subset of 25 newborns including 66 days of video recordings. In incubator, we reach a balanced accuracy of 86%. In open bed, the performance is lower because of a much wider variety of situations whereas less data are available. |