IoT-based incubator monitoring and machine learning powered alarm predictions.

Abstract

Incubators, especially the ones for babies, require continuous monitoring for anomaly detection and taking action when necessary. This study aims to introduce a system in which important information such as temperature, humidity and gas values being tracked from incubator environment continuously in real-time. Multiple sensors, a microcontroller, a transmission module, a cloud server, a mobile application, and a Web application were integrated Data were made accessible to the duty personnel both remotely via Wi-Fi and in the range of the sensors via Bluetooth Low Energy technologies. In addition, potential emergencies were detected and alarm notifications were created utilising a machine learning algorithm. The mobile application receiving the data from the sensors via Bluetooth was designed such a way that it stores the data internally in case of Internet disruption, and transfers the data when the connection is restored. The obtained results reveal that a neural network structure with sensor measurements from the last hour gives the best prediction for the next hour measurement. The affordable hardware and software used in this system make it beneficial, especially in the health sector, in which the close monitoring of baby incubators is vitally important.