Multiple Fault Detection and Smart Monitoring System Based on Machine Learning Classifiers for Infant Incubators Using Raspberry Pi 4

Abstract

The most significant and sensitive component of the biomedical field is the care of newborn newborns. Because of their gestational age or birth weight, certain newborn newborns are at a higher risk of death. Because of their unmet demand for warmth, the majority of preterm newborns born between 32 and 37 weeks of gestation die. The neonatal incubator is a device that provides a regulated and closed environment to preterm newborns. In this paper and depending on technological progress, an intelligent system has been designed to monitor the performance of the incubator sensors depending on four features (temperature, humidity, fan current, and heater current) to detect any fault in the system. The intelligent system employs a low-power computing device to detect the fault in sensors, like the Raspberry Pi 4, which delivers the data from the incubator’s sensors. For classification, tasks adopted many algorithms like Decision Tree (DT), Support Vector Machine (SVM), and Neural Network (NN), then send alerting messages (e-mail, text message) to the nurse or doctor via Wi-Fi. The promising results of the proposed method accuracy come as 98% of DT and SVM while 97.3% of NN.