A 3D pose estimation framework for preterm infants hospitalized in the Neonatal Unit

Infant pose estimation is crucial in different clinical applications, including preterm automatic general movements assessment. Recent infant pose estimation methods are limited by a lack of real clinical data and are mainly focused on 2D detection. We introduce a stereoscopic system for infants’ 3D pose estimation, based on fine-tuning state-of-the-art 2D human pose estimation networks on a large, real, and manually annotated dataset of infants’ images. Our dataset contains over 88k images, collected from 175 videos from 53 premature infants born