Tajin Md Abu Saleh, Hossain Md Shakir, Mongan William M, Dandekar Kapil R
Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104 USA.
Department of Mathematics and Computer Science, Ursinus College, Collegeville, PA 19426, USA.
IEEE Sens J. 2024 Feb;24(3):3863-3873. doi: 10.1109/jsen.2023.3342129. Epub 2023 Dec 18.
Ultra high frequency (UHF) passive radio frequency identification (RFID) tag-based sensors are proposed for intravenous (IV) fluid level monitoring in medical Internet of Things (IoT) applications. Two versions of the sensor are proposed: a binary sensor (i.e., full vs. empty state sensing) and a real-time (., continuous level) sensor. The operating principle is demonstrated using full-wave electromagnetic simulation at 910 MHz and validated with experimental results. Generalized Additive Model (GAM) and random forest algorithms are employed for each interrogation dataset. Real-time sensing is accomplished with small deviations across the models. A minimum of 72% and a maximum of 97% of cases are within a 20% error for the GAM model and 62% to 98% for the random forest model. The proposed sensor is battery-free, lightweight, low-cost, and highly reliable. The read range of the proposed sensor is 4.6 m.
超高频（UHF）无源射频识别（RFID）标签传感器被用于医疗物联网（IoT）应用中的静脉输液液位监测。提出了两种版本的传感器：一种是二元传感器（即满与空状态传感），另一种是实时（即连续液位）传感器。利用910MHz的全波电磁仿真演示了其工作原理，并通过实验结果进行了验证。针对每个询问数据集采用了广义相加模型（GAM）和随机森林算法。通过各模型间的小偏差实现了实时传感。对于GAM模型，至少72%且最多97%的情况误差在20%以内，对于随机森林模型，这一比例为62%至98%。所提出的传感器无需电池、重量轻、成本低且可靠性高。所提出传感器的读取范围为4.6米。
