Respiration is a critical vital sign for infants, and continuous respiratory
monitoring is particularly important for newborns. However, neonates are
sensitive and contact-based sensors present challenges in comfort, hygiene, and
skin health, especially for preterm babies. As a step toward fully automatic,
continuous, and contactless respiratory monitoring, we develop a deep-learning
method for estimating respiratory rate and waveform from plain video footage in
natural settings. Our automated infant respiration flow-based network
(AIRFlowNet) combines video-extracted optical flow input and spatiotemporal
convolutional processing tuned to the infant domain. We support our model with
the first public annotated infant respiration dataset with 125 videos
(AIR-125), drawn from eight infant subjects, set varied pose, lighting, and
camera conditions. We include manual respiration annotations and optimize
AIRFlowNet training on them using a novel spectral bandpass loss function. When
trained and tested on the AIR-125 infant data, our method significantly
outperforms other state-of-the-art methods in respiratory rate estimation,
achieving a mean absolute error of $\sim$2.9 breaths per minute, compared to
$\sim$4.7--6.2 for other public models designed for adult subjects and more
uniform environments.

通过使用深度学习，本研究从普通视频中估计婴儿的呼吸频率和波形，为婴儿呼吸监测提供自动、连续和非接触性的方法。通过构建 AIRFlowNet 模型并使用公开的婴儿呼吸数据集 (AIR-125) 进行训练和测试，结果表明该方法在呼吸频率估计方面明显优于其他公开模型，平均绝对误差为约 2.9 次 / 分钟，而其他设计用于成人主体和更一致环境的模型的平均绝对误差在约 4.7-6.2 次 / 分钟。