Wearable Contactless Respiration Sensor Based on Multi-Material Fibers Integrated into Textile
Bibliographic record
Abstract
In this paper, we report on a novel sensor for the contactless monitoring of the respiration rate, made from multi-material fibers arranged in the form of spiral antenna (2.45 GHz central frequency). High flexibility of the used composite metal-glass-polymer fibers permits their integration into a cotton t-shirt without compromising comfort or restricting movement of the user. At the same time, change of the antenna geometry, due to the chest expansion and the displacement of the air volume in the lungs, is found to cause a significant shift of the antenna operational frequency, thus allowing respiration detection. In contrast with many current solutions, respiration is detected without attachment of the electrodes of any kind to the user's body, neither direct contact of the fiber with the skin is required. Respiration patterns for two male volunteers were recorded with the help of a sensor prototype integrated into standard cotton t-shirt in sitting, standing, and lying scenarios. The typical measured frequency shift for the deep and shallow breathing was found to be in the range 120-200 MHz and 10-15 MHz, respectively. The same spiral fiber antenna is also shown to be suitable for short-range wireless communication, thus allowing respiration data transmission, for example, via the Bluetooth protocol, to mobile handheld 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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 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".