Vital Signs Monitoring in Various Conditions Using the Single Antenna Bio-Sensing Method
Bibliographic record
Abstract
Continuous monitoring of respiration is essential, as it serves as a key biomarker for health assessment. It has a wide range of applications, from medical to industrial settings, including the early detection of severe medical conditions, management of chronic illnesses, and evaluation of worker fatigue in high-risk environments. In this context, Single Antenna Bio-Sensing (SABioS) offers a promising new approach to respiratory monitoring, emphasizing comfort and mobility by removing the need for skin contact, tight-fitting devices, or external readers. This paper investigates the capabilities of SABioS through a series of experiments that assess its effectiveness across different placements, body positions, and distances from the chest. The study also examines the influence of operational frequency using various antenna designs. By analyzing the signal-to-noise ratio (SNR) in diverse scenarios, this research provides valuable insights into the robustness and adaptability of SABioS, paving the way for its development as a practical, non-invasive respiratory monitoring solution.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".