Paper Details

Name: Gurwinder Singh & Rydhm Beri
Country: India

Year: 2025
Volume: Volume-12, Issue-1 (January-June)
Page Number: 202-210
DOI: https://doi.org/10.5281/zenodo.19722949

ABSTRACT
The convergence of Internet of Things (IoT) technologies and Artificial Intelligence (AI) is
reshaping modern healthcare by enabling continuous, intelligent, and automated diagnostic systems. In this study, an advanced monitoring framework is introduced to support maternal and fetal health, particularly in high-risk pregnancies. The system combines IoT-enabled sensors with deep learning techniques to ensure real-time data collection and analysis.

Various physiological parameters of the mother, such as body temperature, blood pressure,
oxygen saturation, and heart rate, along with fetal heart rate, are continuously captured
through interconnected sensors. These sensors are integrated using MICOT hardware
(NodeMCU with MCP3008) and transmit the collected data to a cloud-based platform for storage, monitoring, and predictive analysis.

To enhance the accuracy of identifying potential complications, the study proposes an
optimized one-dimensional Convolutional Neural Network (1D-CNN) model. This model is specifically designed to classify and predict critical maternal and fetal conditions more effectively than traditional approaches.

A dataset comprising approximately 9,000 records was used to validate the system. The
proposed model’s performance was compared against several established machine learning algorithms, including K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machines (SVM), standard Convolutional Neural Networks (CNN), and Extreme Learning Machines (ELM). Evaluation metrics such as accuracy, precision, recall, sensitivity, and F1- score were used to assess performance.

The results demonstrate that the proposed model consistently achieves superior outcomes across all evaluation parameters. These findings suggest that the developed IoT and AI-based system offers a reliable, efficient, and scalable solution for real-time maternal and fetal health monitoring.

Keywords: Health Monitoring, Women Healthcare, Fatal Health