I developed an innovative Hospital Ward Monitoring System using MATLAB App Builder, leveraging the Arduino UNO microcontroller, three ultrasonic sensors, three LM35 temperature sensors, and three heart rate sensors. Our system effectively divided a breadboard into three distinct wards, each monitored for critical patient parameters. The ultrasonic sensors were ingeniously adapted to measure saline levels in IV bottles, providing real-time level indications on the screen. If the saline level fell below a specific threshold, the system triggered a red LED alert, indicating the need for a bottle change, while maintaining a green light for sufficient levels. This real-time saline level monitoring ensures timely intervention, enhancing patient care efficiency.
The system also integrated LM35 temperature sensors to continuously monitor patient temperatures. This data was displayed in real-time on the screen, with the LEDs indicating green for normal ranges and turning red for any deviations, ensuring prompt medical attention. Additionally, heart rate sensors continuously monitored and plotted the real-time BPM of patients. The LEDs provided immediate visual feedback, with green indicating normal heart rates and red highlighting any abnormal readings. The system's capability to provide continuous, automated monitoring of these vital parameters significantly reduces the risk of human error and allows medical staff to respond promptly to any critical changes in a patient's condition.
Our project demonstrated the feasibility and effectiveness of using cost-effective, readily available sensors to create a robust hospital ward monitoring system. By automating the collection and analysis of critical patient data, we aimed to improve overall patient care and enhance the efficiency of medical professionals. The real-time data acquisition and visual feedback provided by our system enable a proactive approach to patient monitoring, facilitating timely interventions and better-informed treatment decisions. This project highlights the potential for integrating advanced sensor technology and data analysis in healthcare settings, paving the way for more efficient and reliable patient monitoring solutions.