Enhancing the Response of a Wearable Sensor for Improved Respiratory Rate (RR) Monitoring
Bibliographic record
Abstract
Currently available devices for monitoring respiratory rate (RR) are cumbersome and expensive (such as capnography and plethysmography), requiring skilled clinicians to operate and analyse. In contrast, the inexpensive and lightweight ones (e.g. reported strain, stretchable belt, and capacitive-based RR sensors) are unable to provide accurate RR measurements due to being highly affected by motion and environmental noise. This study aims to develop a wearable RR sensor with a high sensitivity and accuracy (comparable with capnography) of RR and which is not adversely affected by noise in comparison to other reported sensors in the literature. We have developed a flexible textile-based wearable RR sensor that can be conveniently embedded in a patient’s gown. A design rule was adopted to empirically find the ratio of sensor electrodes with the aim to achieve the highest percentage output frequency change (%F.C). The empirical study also suggests that the %F.C can be further increased by increasing the dielectric material thickness between the sensor and ground electrodes. The enhanced textile-based sensor shows a 10.5% F.C toward the changes in the dielectric constant of a phantom, representing the human chest. The subsequent tests of the proposed RR sensor attached to the torso of the test subject shows a high-frequency variation (43 kHz) between crests (which corresponds to inhaling) and troughs (which corresponds to exhaling), with a 99.39% accuracy compared to the reported strain (a stretchable belt) and capacitive-based RR sensors. In the 35 performed RR tests, the proposed RR sensor picked up 495 crests; during the same tests, the capnograph showed a total of 498 respirations (which leads to 99.39% accuracy). This work demonstrates that the investigation in the electrode ratio and increase of dielectric layer thickness increases the proposed wearable textile base sensor %F.C, which leads to highly reliable (99.39% accuracy) RR detection with high-frequency variations and high SNR, as compared to gold standard capnograph, which is currently used in hospital for respiratory rate monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".