IoT-Based Remote Vital Signs Monitoring and Temperature Forecasting for Pregnant Women

Abstract

The importance of maintaining optimal health during pregnancy for both the mother and fetus has driven the development of numerous artificial intelligence (AI)-based monitoring systems. These systems aim to address the growing need for continuous, reliable health tracking in pregnant women, ensuring early detection of complications and promoting better outcomes. While general-purpose health monitoring platforms exist, there remains a significant gap in solutions explicitly tailored for pregnancy. Addressing this need requires not only real-time monitoring but also predictive capabilities based on vital signs. In this work, we propose an IoT-based pregnancy monitoring system that continuously collects key physiological data, namely body temperature, heart rate, and blood oxygen saturation. The collected data is transmitted in real time and processed using a Long Short-Term Memory (LSTM) neural network to build a model capable of forecasting potential health anomalies. The system provides real-time insights and future predictions. This approach enhances proactive care, enabling timely intervention and improving maternal-fetal health outcomes. This system’s approach shifts between personal and centralized monitoring, a capability particularly valuable where regular prenatal visits are difficult, thereby enhancing the overall effectiveness of prenatal care delivery.