Design and realization of an autonomous multi-powered IOT-based infant incubator monitoring system hardware module

The traditional infant incubators are limited by the absence of real-time remote monitoring, which hinders healthcare providers' ability to promptly assess critical parameters in neonatal environments. Each year, approximately 15 million premature infants are born globally, with nearly one million succumbing to complications. This project aims to design and implement a multi powered IoT-based infant incubator monitoring system to facilitate continuous remote monitoring of vital parameters essential for neonatal care. The system was designed and Arduino Software used for programming the microcontrollers. The system employs ESP32-CAM for video streaming on web server and Blynk IoT for data visualization and storage. The developed hardware module autonomously switches between power sources (5V and 12V) to monitor environmental and biological parameters, including body temperature, heartbeat, blood oxygen saturation, CO₂ concentration, and humidity. Physiological and environmental data is transmitted to the Blynk IoT platform through an ESP8266 module and the infant video capture is also transmitted to web server by the ESP32-CAM module, enabling effective real-time monitoring. Testing confirmed the system's reliability and performance, demonstrating successful data acquisition and transmission. The hardware module was successfully tested, showing no damage and stable functionality on DC backup for 17 hours. Max30100's accuracy slightly lagged behind pulse oximeters, while LM-35 closely matched digital thermometers. Sensor precision was moderate, with standard deviations of 1.79, 1.88, and 0.44. This IoT-based monitoring system addresses the critical need for enhanced neonatal care by allowing healthcare providers to monitor multiple incubators remotely, including video streaming for visual oversight. Despite challenges like stable internet connectivity and the need for technical expertise during setup, the system provides an affordable solution for healthcare facilities, particularly in low-resource settings. Future work should focus on expanding sensor capabilities and improving accessibility to further enhance neonatal outcomes.