Abstract <p>Visual monitoring of vital parameters in premature infants has become an intensively studied area in recent years. Among these parameters, respiration rate (RR) is one of the most critical vital signs, making non-contact measurement of respiration a key research focus. Many published algorithms achieve improved performance when an appropriate region of interest (ROI) is detected prior to RR estimation. Typically, such ROIs are generated using data-driven segmentation methods. However, modern deep learning–based ROI detection algorithms require thousands of annotated samples for training, and manual data collection and annotation are time-consuming and labor-intensive. In this work, we propose a motion–periodicity–based method to automatically generate respiration-related region masks that capture the abdominal or chest area of neonates. The predicted masks were validated against independent expert annotations, achieving high localization consistency on the torso (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(S_{geo}=0.987\)</EquationSource> </InlineEquation>) and significant overlap with clinical ground truth (mean IoU=0.580 and Dice=0.735). We further show that these automatically produced labels can be directly used to train common segmentation architectures, eliminating the need for manual annotation and enabling the creation of large, high-quality datasets for neonatal respiration analysis. Our findings demonstrate that automatic dataset generation is both feasible and effective for training deep learning–based ROI detectors in this domain.</p> Graphical abstract <p></p>