Enhanced Fetal Health Monitoring: Implementation of Django Server for Comprehensive Tracking and Analysis

A woman’s pregnancy has a crucial time in her life that requires careful attention to both the health of the mother and the fetus. The treatment of pregnant women has changed as a result of the convergence of IoT (Internet of Things) and machine learning (ML) technology. IoT integrates smart devices and sensors into everyday objects, facilitating real-time data collection and seamless communication between patients and healthcare providers. Concurrently, ML, a subset of artificial intelligence, analyzes comprehensive datasets, including fetal and maternal heart rates, maternal positioning, temperature, fluid level in the womb, maternal consciousness, and alert messages. The proposed Fetal Health Monitoring System employs Raspberry Pi and Django server technology to develop a comprehensive solution. Integrating various sensors, the system collects real-time data related to the expectant mother’s health, providing continuous monitoring and analysis. By utilizing the capabilities of Django and Raspberry Pi, the platform ensures prompt identification of abnormalities and improves the entire patient experience by providing an intuitive experience for both patients and healthcare professionals. This convergence empowers expectant mothers with personalized advice, early danger detection, and valuable insights, optimizing pregnancy management.