Wearable CSRR-based Sensor for Monitoring Glycemic Levels for Diabetics
Bibliographic record
Abstract
Monitoring glycemia levels in people with diabetes has developed rapidly over the last decade. A broad range of easy-to-use systems of reliable accuracies are now deployed in the market following the introduction of the invasive self-monitoring blood glucose meters (i.e. glucometers) that utilize the capillary blood samples from the fingertips of diabetic patients. However, the limitations and discomforts associated with these painful finger pricking devices have established a new demand for non-invasive pain-free blood glucose monitors to encourage more frequent glucose checks and thereby contribute more generously to diabetes care and prevention. In this study, a novel microwave biosensor is developed in a wearable format to enable non-invasive real-time monitoring of blood glucose level. The design comprises three cells of circular complementary split ring resonators (CSRRs) incorporated in the ground plane of an FR4 dielectric substrate. The passive sensing elements (CSRRs) are excited remotely via a coupled antenna to enable the wearable sensing in a reader/tag configuration. The CSSR-sensor is numerically modeled and analyzed for sensing the glucose concentrations relevant to diabetes condition (60-500mg/dL) by tracking the resonant amplitude variations in the frequency range 1-4GHz. The sensitivity performance of the TP-CSRR tag is practically demonstrated through in-lab measurements using a VNA setup.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".