Published April 13, 2026 | Version v1
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IoT Based Smart Saline Drip Monitoring System Using Load Cell

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Description

 Intravenous (IV) drip therapy is one of the most common clinical procedures performed in healthcare facilities worldwide. Manual monitoring of IV drip bags by nursing staff is resource-intensive, error-prone, and may lead to critical patient safety incidents when drip bags run dry undetected. This paper presents MediDrip, a low-cost, real-time IoT-based intravenous drip monitoring system that employs a high-precision HX711-interfaced load cell to continuously measure the remaining saline volume by weight. The proposed system integrates an ESP32 microcontroller with the Blynk IoT cloud platform to enable wireless data transmission, remote monitoring via a web dashboard, and configurable multi-threshold alert generation. A 1.3-inch OLED display, tri-color LED indicator array (green, yellow, red), and an audible buzzer provide local real-time feedback. Experimental validation was conducted using 100 mL saline bags across 30 test cycles, demonstrating a mean absolute weight error of 1.8 g and percentage level accuracy of ±2.3%. The system successfully triggered remote alerts and local alarms within 1.2 seconds of threshold breach. MediDrip offers a cost-effective, scalable, and clinically practical solution for automated IV drip monitoring, with potential for multi-bed deployment in resource-constrained healthcare environments.

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Dates

Submitted
2026-04-13
Intravenous (IV) fluid therapy remains one of the most fundamental and frequently administered medical interventions in both developed and developing healthcare systems. According to the World Health Organization (WHO), over five billion injections and infusions are administered globally each year, a significant proportion of which involve continuous IV drip administration [1]. Despite this prevalence, the monitoring of IV drip bags in clinical settings continues to rely predominantly on manual observation by nursing personnel, a practice that is inherently susceptible to human error, especially in high patient-to-nurse-ratio environments common in developing nations [2].

References

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