  Objective: Preterm infants' limb monitoring in neonatal intensive care units
(NICUs) is of primary importance for assessing infants' health status and
motor/cognitive development. Herein, we propose a new approach to preterm
infants' limb pose estimation that features spatio-temporal information to
detect and track limb joints from depth videos with high reliability. Methods:
Limb-pose estimation is performed using a deep-learning framework consisting of
a detection and a regression convolutional neural network (CNN) for rough and
precise joint localization, respectively. The CNNs are implemented to encode
connectivity in the temporal direction through 3D convolution. Assessment of
the proposed framework is performed through a comprehensive study with sixteen
depth videos acquired in the actual clinical practice from sixteen preterm
infants (the babyPose dataset). Results: When applied to pose estimation, the
median root mean squared distance, computed among all limbs, between the
estimated and the ground-truth pose was 9.06 pixels, overcoming approaches
based on spatial features only (11.27pixels). Conclusion: Results showed that
the spatio-temporal features had a significant influence on the pose-estimation
performance, especially in challenging cases (e.g., homogeneous image
intensity). Significance: This paper significantly enhances the state of art in
automatic assessment of preterm infants' health status by introducing the use
of spatio-temporal features for limb detection and tracking, and by being the
first study to use depth videos acquired in the actual clinical practice for
limb-pose estimation. The babyPose dataset has been released as the first
annotated dataset for infants' pose estimation.
