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

ABSTRACT

BACKGROUND: Incubators, especially the ones for babies, require continuous monitoring for anomaly detection and taking action when necessary. OBJECTIVE: 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. METHOD: 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. RESULTS: 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. CONCLUSION: 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.