Infant Monitoring System for Real-Time and Remote Discomfort Detection

Discomfort detection for young infants is essential, since they lack the ability to verbalize their pain and discomfort. In this paper, we propose a novel infant monitoring system, enabling continuous monitoring for infant discomfort detection. The proposed algorithm is robust to arbitrary head rotations, occlusions and face profiles. For this purpose, a Faster RCNN architecture is first pre-trained with the ImageNet dataset, and then fine-tuned with a training dataset of different infant expressions. Our proposed method obtains a mean average precision of 74.4% and 87.4% for classifying infant expressions. The presented system enables reflux disease analysis and remote home monitoring in a more relaxed environment, which is largely preferred by pediatricians and parents.