Affiliations: Department of Electrical and Electronics Engineering, Izmir University of Economics, Izmir, Turkey
Correspondence:
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Corresponding author: Ayca Kumluca Topalli, Department of Electrical and Electronics Engineering, Izmir University of Economics, 35330, Balcova, Izmir, Turkey. E-mail: ayca.topalli@ieu.edu.tr.
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.
Keywords: Biomedical, cloud service, healthcare, incubators, machine learning, mobile applications, web application, child wellbeing
