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Internet of things-based intravenous fluid monitoring system prototype and drops per minute control with MG996R servo

Year: 2025
Type: article
Abstract: A medical device called an intravenous is used to restore electrolyte balance in hospitalized patients with various diseases by replacing lost fluids. When a patient has an emergency, such as dehydration, seizures, high fever, malaria, diarrhea, or other illnesses, an IV is usually required. In health facilities, the practice of intravenous monitoring by healthcare workers serves as an essential part of treatment. This is especially important because delays in intravenous changes or differences in intravenous flow rates in patients can be fatal to the treated patient. A system of tools is needed to facilitate nurses in monitoring the condition of the intravenous volume, and the number of intravenous droplets in the patient. Intravenous volume, the number of intravenous droplets per minute of the treated patient, and notification of intravenous fluid running out. This intravenous fluid monitoring system uses a Loadcell sensor to measure intravenous volume, an Infrared sensor to detect intravenous droplets, and an MG996r servo motor to adjust the speed of intravenous fluid drops automatically. The web monitoring platform of this system is used to display the condition of intravenous volume and droplets per minute in real time. From the test results, the average value of Loadcell accuracy in measuring the mass of objects on 25 data reaches 99.08% and the average value of the accuracy of the MG996r Servo in adjusting the speed of the intravenous drops on 5 test data reached 96.96%. The Liquid Crystal Display will display the volume of intravenous fluid, drop per minute (DPM), and counter, as well as the selected condition between adult or child, while the Web will display the volume of the intravenous fluid, DPM, and pop-up notification that the intravenous fluid will soon run out when the intravenous fluid read by loadcell less than 100ml.
DOI: 10.31603/biseeng.374
Language: English
Open Access status: hybrid
OpenAlex ID: W4412384388
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