Child Health Monitoring Through Remote Sensing Technologies And Mobile Healthcare Applications

Abstract

Background: Child health surveillance is an important tool for early detection and avoidance of disease, especially in underserved and remote areas where access to health care services is limited. Advancements in remote sensing technologies, wearable gadgets, and mobile healthcare (mHealth) use cases offer fresh opportunities for uninterrupted pediatric health surveillance. Objective: The present study aims to develop a comprehensive child health monitoring framework integrating remote sensing technologies, wearable sensors and mobile healthcare applications in order to enable real-time health assessment as well as disease risk prediction. Methodology: The suggested framework collects physiological data including heart rate, body temperature and oxygen saturation compared to wearable devices and environmental data are collected using remote sensing platforms. The data collected is then processed by employing machine learning algorithms such as Random Forest, XGBoost and Long Short Term Memory (LSTM) models to predict possible health hazards and suggest early health care. Findings: We experimentally validated with a dataset of 5000 children and the LSTM model achieved the best prediction accuracy of 96.1% which is higher than the Random Forest (92.4%) along with XGBoost (94.3%). The framework also achieved a high disease detection accuracy of 97.1% for fever illnesses and 95.2% for respiratory diseases. Conclusion: The proposed system effectively combines remote sensing and mobile healthcare technologies to offer precise, real-time monitoring of child health, facilitating early intervention and enhancing healthcare access.