An Intelligent Security for Preventing Infant Abduction and Ensemble Metaheuristics Optimization Algorithm for Data Regression

Abstract

Newborn infant care is the most crucial and delicate area of biomedicine. Towards the goal of better infant care, a prototype is created that provides a dependable and efficient monitoring system. In this study, we explore the use of non-invasive sensors to construct a smart health monitoring system in an incubator. Health monitoring sensor system that simultaneously measures blood saturation levels (SpO2), heart rate, electrocardiogram (ECG), motion rate, JAUNDICE, weeping monitor system, and body temperature is designed and exhibited. This technology can identify the presence of harmful gases in the incubator in addition to monitoring the vital signs of the child. Because of its stability and simple plug-and-play functionality, the embedded system is built on the Arduino platform. With the help of a Wi-Fi module, the measured vitals are sent to the mobile application, where they can reassure worried parents and medical professionals. This method also verifies that the newborn is safe, which means that the infant's location can be required in the program by means of a GPS module, making it less likely that infants would be kidnapped or stolen. After the health monitoring sensor system has gathered the important parameters, the next step is to analyze the data than an ensemble metaheuristics optimization algorithm is used to do regression analysis, and statistical tests like ANOVA test have been done to show that the proposed algorithm is better and more powerful.