Characterization of Wearable Respiratory Sensors for Breathing Parameter Measurements
Bibliographic record
Abstract
Controlling and treating respiratory diseases requires monitoring of the respiratory system. Conventional monitoring systems lack portability, which makes continuous monitoring difficult. The accuracy and reliability of portable monitoring technologies, such as wearable sensors, have rarely been characterized. This study characterized measurements of a commercial respiratory sensor in measuring tidal volume, inhalation time, and respiratory rate (RR) based on the measurement of abdominal/thoracic movements. The respiratory sensor was evaluated when placed at the thoracic spine (at the T6 and T12 vertebrae) and the abdomen (at the L3 vertebra) to find a suitable sensor location. Each test also included three breathing patterns (shallow normal, and deep) to calibrate the respiratory sensor against a flow sensor. The results showed that the respiratory sensor placement on either thorax location offered more accurate tidal volume estimation. Additionally, implementing an individualized calibration formula outperformed using a universal formula for all participants. The study involved two visits for each participant to assess how accurately the calibration formulation for estimating tidal volume, determined during the first visit, was performed during the second visit. The maximum mean relative error of tidal volume increased from 19% at the first visit to 35% at the second visit. The mean relative error of respiration rate and inhalation time remained below 13% and 26%, respectively, across all breathing patterns studied. This highlights the necessity of recalibration before each measurement to obtain more accurate tidal volume estimations. These observations may explain the extent of reliability of the wearable respiratory sensor for at-home daily monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".