IEEE J Biomed Health Inform. 2021 May;25(5):1429-1440. doi: 10.1109/JBHI.2020.3037031. Epub 2021 May 11.
The manual monitoring of young infants suffering from diseases like reflux is significant, since infants can hardly articulate their feelings. In this work, we propose a video-based infant monitoring system for the analysis of infant expressions and states, approaching real-time performance. The expressions of interest consist of discomfort, unhappy, joy and neutral, whereas states include sleep, pacifier and open mouth. Benefiting from the expression analysis, the discomfort moments can also be used and correlated with a symptom-related disease, such as a reflux measurement for the diagnosis of gastroesophageal reflux. The system consists of three components: infant expressions and states detection, object tracking and detection compensation. The proposed system is based on combining expression detection using Fast R-CNN with a compensated detection using analyzing information from the previous frame and utilizing a Hidden Markov Model. The experimental results show a mean average precision of 81.9% and 84.8% for 4 infant expressions and 3 states evaluated with both clinical and daily datasets. Meanwhile, the average precision for discomfort detection achieves up to 90%.
对于患有反流等疾病的婴儿，手动监测非常重要，因为婴儿很难表达自己的感受。在这项工作中，我们提出了一种基于视频的婴儿监测系统，用于分析婴儿的表情和状态，以实现实时性能。感兴趣的表情包括不适、不开心、高兴和中性，而状态包括睡眠、奶嘴和张口。得益于表情分析，不适时刻也可以被利用，并与与症状相关的疾病相关联，例如反流测量用于诊断胃食管反流。该系统包括三个组件：婴儿表情和状态检测、目标跟踪和检测补偿。所提出的系统基于使用 Fast R-CNN 进行表情检测，并结合使用前一帧的信息进行补偿检测，同时利用隐马尔可夫模型。实验结果表明，使用临床和日常数据集评估了 4 种婴儿表情和 3 种状态，平均准确率分别为 81.9%和 84.8%。同时，不适检测的平均准确率高达 90%。
