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    Area of Science:

    • Biomedical Engineering
    • Electromagnetics
    • Sensor Technology

    Background:

    • Non-invasive glucose monitoring is crucial for diabetes management.
    • Existing methods face challenges with accuracy and stability.
    • Millimeter wave (MMW) frequencies offer potential for sensitive and deep tissue penetration.

    Purpose of the Study:

    • To develop an adaptive, non-invasive system for estimating glucose concentration.
    • To leverage millimeter wave transmission magnitude and phase data for glucose sensing.
    • To create a system robust to variations in measurement conditions.

    Main Methods:

    • Utilized Debye relaxation model to identify optimal millimeter wave frequencies (60-80 GHz).
    • Developed a single output-neuron complex-valued neural network (CVNN) for adaptive estimation.
    • Trained and validated the CVNN using transmission magnitude and phase data from glucose water solutions (0-300 mg/dL).

    Main Results:

    • Demonstrated that changes in glucose concentration correlate with variations in transmission magnitude and phase.
    • The CVNN successfully learned the complex relationship between MMW transmission data and glucose levels.
    • The system exhibited good generalization for estimating unknown sample concentrations.

    Conclusions:

    • The proposed adaptive system effectively estimates glucose concentration non-invasively within the clinically relevant range.
    • The CVNN's learning capability provides adaptability to different measurement conditions, addressing instability issues.
    • This millimeter wave-based approach shows promise for reliable and stable glucose monitoring.