研究目的
To develop a method for non-invasive glucose concentration detection by analyzing microwave transmission characteristics through glucose solutions using a complex-valued neural network.
研究成果
The CVNN-based approach effectively predicts glucose concentrations with high accuracy by leveraging microwave transmission characteristics, demonstrating potential for non-invasive glucose monitoring applications.
研究不足
The method may be sensitive to environmental factors and requires calibration. It is tested only in controlled laboratory conditions and may not account for all biological variabilities in real-world applications.
1：Experimental Design and Method Selection:
The study uses a microwave transmission setup to measure S-parameters of glucose solutions at various concentrations. A complex-valued neural network is designed to process the magnitude and phase of S21 parameters for glucose prediction.
2：Sample Selection and Data Sources:
Glucose solutions with concentrations ranging from 0 to 300 mg/dL are prepared and measured.
3：List of Experimental Equipment and Materials:
A vector network analyzer (VNA) is used for S-parameter measurements. Glucose solutions are contained in a sample holder.
4：Experimental Procedures and Operational Workflow:
S-parameters are measured across a frequency range (e.g., 60-80 GHz). Data is preprocessed and fed into the CVNN for training and validation.
5：Data Analysis Methods:
The CVNN processes complex inputs (magnitude and phase deviations) to output glucose concentration predictions. Performance is evaluated based on accuracy and error metrics.
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