An Embedded IoT Architecture for Continuous Output Pressure Surveillance in Medical Oxygen Concentrators
Description
The increasing demand for portable and reliable medical oxygen delivery systems has highlighted the need for continuous monitoring of key performance parameters, particularly output pressure, to ensure patient safety and device efficiency. Traditional oxygen concentrators often lack real-time monitoring capabilities, making it difficult to detect pressure anomalies that may compromise therapeutic effectiveness. To address this limitation, a cost-effective, IoT-enabled monitoring system has been developed. The proposed system integrates a 1.2 MPa atmospheric pressure sensor with an HX710B analog-to-digital converter to accurately measure output pressure levels. An ATmega328 microcontroller processes the data and displays real-time readings on an LCD screen, while an ESP8266 module transmits the information to the Blynk cloud platform for remote access and data logging. This architecture allows healthcare providers and users to receive immediate alerts when pressure deviates from safe thresholds, enabling timely maintenance and minimizing risks. The system demonstrates stable performance, reliable wireless communication, and effective pressure monitoring. Its low cost, ease of integration, and remote accessibility make it well-suited for home healthcare, telemedicine applications, and smart medical device development.
Files
Steps to reproduce
The system utilizes a 1.2 MPa stainless steel pressure transducer interfaced with an HX710B analog-to-digital converter, with an ESP8266 microcontroller handling data acquisition and wireless transmission. The collected pressure readings are transmitted to a cloud platform, such as Blynk, for remote monitoring and data logging. Calibration was performed using a standard air pressure regulator and a reference pressure gauge to ensure accurate measurements, with calibration equations applied to convert raw ADC values into pressure readings in MPa. A moving average filter was implemented to minimize noise and enhance precision. The ESP8266 was programmed using the Arduino IDE with the Blynk library, transmitting pressure data every second while incorporating a threshold-based alert system to notify users of abnormal fluctuations. Data collection was conducted under controlled conditions with varying flow rates (1–3 LPM), and environmental factors such as humidity were monitored for consistency. The hardware setup involves assembling the components as per the schematic, while the software configuration includes programming the ESP8266 and ensuring proper calibration. Logged data is extracted from the cloud for analysis using Python or Excel to visualize trends. However, the system's accuracy depends on sensor calibration, and network reliability affects wireless transmission performance.
Institutions
- Karunya University