Fetal Health Monitoring Belt using the Internet of Things (IoT) and Machine Learning

Abstract

The Fetal Health Monitoring Belt using IoT (Internet of Things) and ML (Machine Learning) is a novel approach to monitor and assess the well-being of the foetus during pregnancy. The belt incorporates various sensors, including a Doppler ultrasound transducer for fetal heart rate monitoring, an Electromyography (EMG) sensor for uterine contraction detection, and additional sensors for temperature, SpO2, and Electrocardiography (ECG) measurements. The collected data is transmitted wirelessly to a centralized IoT platform for real-time analysis and monitoring. Machine learning algorithms are employed to analyse the data and detect anomalies or patterns indicative of fetal distress or maternal health issues. This integrated system offers continuous, remote, and personalized monitoring, enabling early detection and intervention to improve maternal and fetal outcomes.