A 4-Channel Piezo Transducer Based Flexible Hybrid Sensor for Respiratory Monitoring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Monitoring of breathing under different physical conditions is important in personal health tracking. Many researchers showed the importance of respiratory rate (RR) monitoring as an early anticipation of a serious problem in hospitals [1] [2] . There are some solutions for respiratory monitoring that are not ideal as a wearable monitoring system due to their sensitivity to body motions [3] [4] . A novel method of ultrasound based respiratory monitoring was reported before [5] [7] . The system monitors the diaphragm motions using ultrasound waves. However, the device error was higher in tests where body was under big motions resulting in sensor displacements due to skin stretch. Arm abduction and adduction had the highest amount of skin artifacts. In this work, we show an advancement in a respiratory monitoring system based on diaphragm wall motion tracking which is less sensitive to motion artifacts. Diaphragm motions are measured by a designed flexible hybrid ultrasound sensory system with four ultrasound PZT5 piezo transducers. Use of flexible hybrid electronics allows integrating both rigid electronics and printed materials on a flexible substrate resulting in thin, lightweight and conformal device. Conformal interface to human skin significantly increases the signal-to-noise ratio for the measurement of our respiratory signal. We evaluate the accuracy and robustness of the sensory system. Measurements are referenced to an SPR-BTA spirometer. All tests are done in non-stationary human body situations to evaluate the sensor in a real life.
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.
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 | 0.000 | 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 | 0.000 |
| 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 it