Non-invasive blood sugar detection by cost-effective capacitance spectroscopy
Bibliographic record
Abstract
Capacitance spectroscopy is a promising technique for detecting small changes in electrical properties of human blood such as conductivity, permittivity, capacitance, and dielectric constant due to the change of glucose concentration. We studied the capacitance of tissue-mimicking phantoms and the human body, in vitro and in vivo, for detecting blood sugar levels non-invasively by a simple and cost-effective setup. We found that, in tissue-mimicking phantoms, capacitance decreased ∼19 % for glucose concentration increases of 85 % with a correlation coefficient of R 2 =0.96. In the oral meal tolerance test (OMTT), the body capacitance increased less than 9 % for a 50 % increase in blood sugar level, and it followed the invasive reference with a lag time of ∼25–45 min and semi-invasive reference with a nominal time delay. This lag time is associated with the food digestion time and the diffusion time for the glucose to reach interstitial fluid from blood vessels. We also studied different types of metal pads made of copper, gold-coated copper, and aluminum with various sizes for system optimization. Considering the simplicity, low cost, easy operation, and moderate performance, this capacitive spectroscopy could potentially be a promising technique of detecting blood sugar levels and could be incorporated into other blood sugar detection techniques to reinforce the overall performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".