NICU-Care Dataset

NICU-Care is a high-quality video dataset designed to support visual recognition tasks in Neonatal Intensive Care Unit (NICU) scenarios, including nursing action recognition, object detection, and semantic segmentation. It was constructed in a standardized simulated NICU environment, capturing multi-view RGB videos of professional nurses performing six types of routine caregiving procedures on simulated infants. The dataset provides fine-grained temporal annotations and pixel-level segmentation masks for key objects like nurse hands, medical tools, and infant body parts. It replicates real-world clinical workflows and adopts a structured annotation system to support diverse research objectives. The dataset's design, annotation methodology, and task adaptability were rigorously validated using mainstream deep learning models. A unified data format and storage scheme ensure structured management and reusability. The public release of NICU-Care is expected to facilitate automated nursing assessment, intelligent monitoring, and data-driven training, with future extensions planned to incorporate richer annotations and multimodal sensory information to further increase its scientific and practical value.