  Non-nutritive sucking (NNS), which refers to the act of sucking on a
pacifier, finger, or similar object without nutrient intake, plays a crucial
role in assessing healthy early development. In the case of preterm infants,
NNS behavior is a key component in determining their readiness for feeding. In
older infants, the characteristics of NNS behavior offer valuable insights into
neural and motor development. Additionally, NNS activity has been proposed as a
potential safeguard against sudden infant death syndrome (SIDS). However, the
clinical application of NNS assessment is currently hindered by labor-intensive
and subjective finger-in-mouth evaluations. Consequently, researchers often
resort to expensive pressure transducers for objective NNS signal measurement.
To enhance the accessibility and reliability of NNS signal monitoring for both
clinicians and researchers, we introduce a vision-based algorithm designed for
non-contact detection of NNS activity using baby monitor footage in natural
settings. Our approach involves a comprehensive exploration of optical flow and
temporal convolutional networks, enabling the detection and amplification of
subtle infant-sucking signals. We successfully classify short video clips of
uniform length into NNS and non-NNS periods. Furthermore, we investigate manual
and learning-based techniques to piece together local classification results,
facilitating the segmentation of longer mixed-activity videos into NNS and
non-NNS segments of varying duration. Our research introduces two novel
datasets of annotated infant videos, including one sourced from our clinical
study featuring 19 infant subjects and 183 hours of overnight baby monitor
footage.
