To address these challenges, hospitals that follow developmentally supportive care models have adopted frameworks such as the Newborn Individualized Developmental Care and Assessment Program (NIDCAP). In these settings, medical staff strive to maintain a calm and nurturing environment that respects the infants' behavioral cues. Caregivers monitor the sleep and activity states of the babies according to the NIDCAP scale, which is further informed by the FINE (Family and Infant Neurodevelopmental Education) tutorial materials developed by NIDCAP-certified experts [8]. However, manual monitoring of sleep and activity phases is time-consuming, labor-intensive, and prone to subjectivity. This presents a barrier to widespread implementation, especially in resource-constrained settings. From a clinical perspective, automating the classification of neonatal behavioral states according to the NIDCAP framework is a highly worthwhile objective, offering both practical and developmental benefits. Such systems can provide continuous, objective observation of infants' sleep and activity states, enabling more timely and individualized care. Real-time classification allows healthcare professionals to better synchronize caregiving tasks--such as feeding, medical procedures, or environmental adjustments--with the infant's most receptive states, thereby minimizing stress and supporting improved developmental outcomes. Furthermore, automated monitoring facilitates earlier detection of abnormal patterns, reduces the burden on clinical staff, and promotes a more structured and developmentally sensitive workflow. Ultimately, the integration of intelligent monitoring tools has the potential to transform neonatal care from reactive to proactive, optimizing both clinical efficiency and long-term health trajectories. Advancements in machine learning, especially in the field of deep learning, have opened new possibilities for healthcare applications, including neonatal care. Recent studies have explored the use of neural networks for analyzing video, audio, and physiological data to identify behavioral states or detect early signs of distress. Recurrent neural networks (RNNs), particularly gated recurrent units (GRUs) and long short-term memory (LSTM) architectures, have shown strong capabilities in modeling temporal sequences, making them ideal for understanding sleep-wake patterns and physiological fluctuations over time. In neonatal care, combining computer vision with physiological signal processing offers a promising avenue to reduce the reliance on subjective assessments and to enhance the continuity and precision of monitoring. In this study, we propose a novel approach to address this challenge using a GRU-stack-based method that determines the activity and sleep phases based on visual features extracted from camera footage. In addition, we demonstrate that integrating physiological signals--specifically pulse rate and pulse rate variability derived from photoplethysmography (PPG) recordings from standard patient monitors--significantly enhances the accuracy and reliability of the classification. Our work represents an important step toward non-invasive, automated, and scalable solutions for developmental care in neonatal settings. Behavioral monitoring via the NIDCAP protocol is crucial for optimizing newborn and preterm infant development, offering comprehensive insights beyond traditional vital signs to support critical brain development, as sleep duration significantly impacts neural circuit formation. Clinically, this approach enables personalized care, precisely timing medical interventions during an infant's most receptive states to minimize stress and maximize therapeutic efficacy, with early behavioral patterns serving as valuable predictors for later cognitive and neurological outcomes. The integration of a GRU-stack-based automated system significantly enhances monitoring accuracy and continuity, eliminating labor-intensive manual observations and providing continuous 24-h surveillance unattainable by human observation alone.