Neonatal Behavioral Monitoring Dataset: Face-Cropped Images from NICU for preterm babies classification

This dataset consists of face-cropped images of neonates captured in a Neonatal Intensive Care Unit (NICU) environment, designed to support vision-based behavioral monitoring research. It focuses on two primary binary classification tasks: distinguishing between sleep and awake states, and identifying crying versus normal (non-crying) behaviors. The images were curated from publicly available sources, emphasizing real-world NICU conditions such as variable lighting, occlusions, and subtle facial cues relevant to low-resource settings. The dataset is structured into two subsets: Sleep/Awake Subset: Contains 1,336 images labeled based on visual criteria. Sleep: 733 images (characterized by eyes closed and minimal movement). Awake: 603 images (characterized by eyes open and visible activity). Crying/Normal Subset: Contains 4,338 images labeled using indicators like mouth openness, facial strain, and contextual cues. Crying: 1,971 images. Normal: 2,367 images. All images are preprocessed as face-cropped to focus on relevant facial features, facilitating efficient model training for edge devices. This dataset was utilized in the development and evaluation of BabyEHANet, a hybrid attention network for real-time neonatal monitoring, as described in the associated paper: "BabyEHANet: Dual-Residual Hybrid Attention for Real-Time Vision-Based Neonatal Behavioral Monitoring on Edge Devices." The dataset is intended for researchers in computer vision, neonatal care, and edge computing, enabling advancements in automated distress detection and behavioral analysis. It promotes accessibility in resource-constrained environments by providing a benchmark for models optimized for hardware like Raspberry Pi.