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AI-Enabled Smart System for Continuous Monitoring of Neonatal Vital Signs in Intensive Care Unit
by , , , ,
International Journal of Technology and Emerging Research 2025 , 1 (1) , 82–88
10.64823/ijter.2501012Abstract
Neonates in Intensive Care Units (NICUs) require continuous monitoring of vital signs to detect early signs of distress. Traditional methods rely on human observation and wired equipment, which can lead to delayed responses and increased workload for medical staff. With advancements in Artificial Intelligence (AI) and the Internet of Things (IoT), there is growing potential to improve neonatal care through smart, real-time monitoring systems. This project involves the design and development of an AI-enabled neonatal monitoring system using IoT-based wearable sensors. These sensors measure key health parameters—heart rate, temperature, and oxygen saturation (SpO₂)—and transmit the data wirelessly for analysis. The AI component uses pattern recognition and threshold-based logic to detect anomalies and trigger alerts. A mobile application interface allows caregivers to receive notifications in real time.Preliminary simulations and prototype testing demonstrate that the system can continuously monitor vital signs and generate timely alerts. While clinical testing is pending, early evaluations suggest reduced false alarms and faster anomaly detection compared to manual methods. The proposed system offers a promising tool to support NICU staff by providing uninterrupted, intelligent monitoring of critical neonatal parameters. Once fully validated, it could enhance care efficiency, reduce human error, and improve outcomes for vulnerable newborns.
Keywords: artificial intelligence, IoT, Neonatal Monitoring, NICU, Vital Signs, Smart Healthcare.
© 2025 The Author(s). Published by IORO Publications. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original author and source are credited, a link to the license is provided, and any changes are indicated.
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