Single Antenna Bio-Sensing for Noninvasive Respiratory and Cardiac Activity Monitoring
Bibliographic record
Abstract
The single antenna bio-sensing (SABioS) method is investigated for noninvasive respiratory and cardiac activity monitoring. Using an antenna sensor installed over the chest of a subject, the vital signs can be captured by analyzing changes in the antenna’s reflection coefficient due to two contributors, being the variations in the dielectric composition of the body and the structural deformations of the antenna due to thoracic expansion during the respiratory cycle. The operation principle of this method is elaborated and validated through simulations, and its agreement with a medical-grade reference device is studied via a preliminary experimental setup involving 14 volunteers. The results were analyzed using Bland-Altman analysis, linear regression, and various error metrics, and the maximum calculated mean absolute errors (MAEs) were 0.06 breaths per minute (bpm) for breathing rate (BR) and 0.14 s for inspiration/expiration time, demonstrating a strong agreement with the medical reference device. The article also briefly explores the potential of SABioS in cardiac monitoring and detecting breathing pauses, as well as its versatility in operating with different antenna types. The SABioS method provides a noninvasive, comfortable, and accurate solution for continuous vital sign monitoring without any dependency to external devices.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".