Advancing NICU Monitoring with IoT and Real-Time AI Analytics

In neonatal intensive care units (NICUs), the prompt and accurate observation of sick and premature infants is of the most critical significance. Using fuzzy logic and the Internet of Things (IoT), this research demonstrates an innovative method for NICU monitoring. Our system constantly records data on critical factors, including temperature, oxygen saturation, and heart rate, via an IoT sensor network. To facilitate proactive management of newborn health, this data is analysed using Artificial Intelligence (AI) algorithms based on fuzzy logic. The results of this processing include predictive analytics and real-time insights. To provide more adaptable and precise decision-making assistance, fuzzy logic is used to deal with physiological data's inherent uncertainties and unpredictability. The proposed approach aims to have a more responsive NICU staff, better patient outcomes, and fewer critical events. This integrated strategy is useful for improving NICU operations and providing the best care for premature newborns via clinical trials and simulations.