Infant Monitoring System for Real-Time and Remote Discomfort Detection

Abstract

Infant monitoring is very important in daily life, since infants lack the ability to verbalize and communicate with their caregivers. However, a full-time monitoring provided by caregivers or practitioners is very expensive and labor intensive. To solve the practical difficulties for particular nighttime monitoring and further reduce the labor cost, in this article, we propose a novel 24-hour infant monitoring system, enabling continuous monitoring for infant discomfort detection using both Red-Green-Blue (RGB) and Near Infrared-light (NIR) videos. The proposed algorithm is robust to arbitrary head rotations, occlusions and face profiles. For this purpose, a Faster R-CNN architecture is first pre-trained with the ImageNet dataset, and then fine-tuned with a specific training dataset of different infant expressions. Our proposed method obtains an Average Precision of more than 87% for classifying discomfort expression. Infant monitoring during the nighttime using NIR videos is proposed for the first time, and the presented system enables reflux disease analysis and continuous remote home monitoring in a more relaxed environment, which is largely preferred by pediatricians and parents.