Automatic Infant Respiration Estimation from Video: A Deep Flow-based
  Algorithm and a Novel Public Benchmark
Sai Kumar Reddy Manne Shaotong Zhu Sarah Ostadabbas Michael Wan
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.