A Compact Dielectric Resonator Antenna Based Microwave Sensor for Non-Invasive Blood Glucose Monitoring
Bibliographic record
Abstract
One of the major challenges with managing diabetes is non-invasive measurement and accurate reading. Microwave based measurements, especially Dielectric Resonator Antennas (DRAs) are capable to catch up the variation in blood glucose levels with the variation in tissue permittivity and frequency shift. The aim of this research work is to investigate the proof of concept for non-invasive blood glucose monitoring using a Cylindrical Dielectric Resonator Antenna (DRA). The proposed CDRA operates at a frequency of 4.187 GHz, utilizing a high-dielectric permittivity (εr = 9.9) resonator material. The sensing method involves positioning the patient's thumb over the resonator, where fluctuations in blood permittivity caused by glucose concentration modify the input impedance, resulting in a detectable frequency shift. A three-dimensional (3D) electromagnetic model of the human thumb has been designed and simulated in CST Microwave Studio at 4.75 GHz, utilizing the Cole–Cole dispersion model to examine the interaction between the CDRA sensor and biological tissue. The proposed antenna achieved a simulated gain of 6.509 dBi and a directivity of 6.964 dBi which is very good while compared with microstrip antennas. The frequency shifts associated with differences in permittivity proves the potential for non-invasive detection of glucose level changes. The sensor's sensitivity against Cole-Cole model is estimated to be 1.87 kHz/mg/dL and the estimated sensitivity of aqueous glucose solution is approximately 0.7 MHz/mg/dL. The proposed CDRA-based blood glucose sensing system effectively correlates resonant frequency fluctuations with glucose concentration, revealing significant potential for future incorporation into compact, wearable, and real-time glucose monitoring device.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".