Guodao Zhang , Qiwen Zhang , Chaochao Wang , Xiaojun Ji , Zhengqiu Weng , Abdulilah Mohammad Mayet , Xinjun Miao
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引用次数: 0
Abstract
In this study, a microstrip sensor with U-shaped resonators was designed and developed for non-invasive blood glucose measurement. The sensor comprises five U-shaped resonators forming a microstrip circuit, transmitting in the frequency ranges of 2–2.3 GHz and 3.9–4.2 GHz. Glucose solutions with concentrations from 50 mg/dL to 500 mg/dL were prepared and placed in a 3D-printed PLA container for testing. Samples were positioned non-contact on the sensor, and frequency response changes up to 6 GHz were recorded. The sensor exhibited high sensitivity, measuring 1.50 MHz per mg/dL. Due to environmental susceptibilities and the relatively low repeatability of microstrip sensors, each sample was tested five times, yielding a total of 50 measurements. These recorded responses were processed using a multilayer perceptron (MLP) neural network, which predicted glucose concentrations with a mean relative error (MRE%) of less than 2.48 %. This research highlights the development of a highly sensitive, non-invasive glucose sensor integrated with a neural network, offering precise and rapid blood glucose measurements. This system holds promise as a valuable tool for managing diabetes and other blood glucose-related conditions.
期刊介绍:
Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.