  Preterm infants' limb-pose estimation is a crucial but challenging task,
which may improve patients' care and facilitate clinicians in infant's
movements monitoring. Work in the literature either provides approaches to
whole-body segmentation and tracking, which, however, has poor clinical value,
or retrieve a posteriori limb pose from limb segmentation, increasing
computational costs and introducing inaccuracy sources. In this paper, we
address the problem of limb-pose estimation under a different point of view. We
proposed a 2D fully-convolutional neural network for roughly detecting limb
joints and joint connections, followed by a regression convolutional neural
network for accurate joint and joint-connection position estimation. Joints
from the same limb are then connected with a maximum bipartite matching
approach. Our analysis does not require any prior modeling of infants' body
structure, neither any manual interventions. For developing and testing the
proposed approach, we built a dataset of four videos (video length = 90 s)
recorded with a depth sensor in a neonatal intensive care unit (NICU) during
the actual clinical practice, achieving median root mean square distance
[pixels] of 10.790 (right arm), 10.542 (left arm), 8.294 (right leg), 11.270
(left leg) with respect to the ground-truth limb pose. The idea of estimating
limb pose directly from depth images may represent a future paradigm for
addressing the problem of preterm-infants' movement monitoring and offer all
possible support to clinicians in NICUs.
