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Terahertz Microfluidic Sensing Using a Parallel-plate Waveguide Sensor

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Passive UHF RFID-based real-time intravenous fluid level sensor.

Md Abu Saleh Tajin¹, Md Shakir Hossain¹, William M Mongan²

¹Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104 USA.

IEEE Sensors Journal

|August 12, 2024

Summary

This summary is machine-generated.

Ultra high frequency (UHF) passive radio frequency identification (RFID) sensors offer reliable, battery-free intravenous fluid level monitoring for medical Internet of Things (IoT) devices. These sensors provide continuous or binary state sensing with high accuracy and a 4.6 m read range.

Keywords:

Fluid level sensor IoT (Internet of Things)RFID (Radio Frequency Identification)machine learning passive sensor smart transfusion

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Area of Science:

Medical device technology
Wireless sensor networks
Internet of Things (IoT)

Background:

Intravenous (IV) fluid level monitoring is critical in healthcare.
Current methods may lack real-time, continuous, or non-invasive capabilities.
Integration of wireless sensors in medical IoT applications is growing.

Purpose of the Study:

To propose and validate ultra high frequency (UHF) passive radio frequency identification (RFID) tag-based sensors for IV fluid level monitoring.
To develop both binary (full/empty) and real-time (continuous) sensing capabilities.
To assess the performance of machine learning models for fluid level estimation.

Main Methods:

Electromagnetic simulation at 910 MHz to demonstrate operating principles.
Experimental validation of the proposed sensor designs.
Application of Generalized Additive Models (GAM) and random forest algorithms to interrogation data.

Main Results:

Real-time sensing achieved with minimal deviations between GAM and random forest models.
Accuracy within 20% error achieved for 72%-97% of cases (GAM) and 62%-98% (random forest).
The sensor is battery-free, lightweight, low-cost, reliable, with a 4.6 m read range.

Conclusions:

UHF passive RFID sensors are a viable solution for accurate IV fluid level monitoring.
The proposed sensors integrate seamlessly into medical IoT frameworks.
The technology offers a cost-effective, reliable, and non-invasive alternative for fluid management.