Additively Manufactured High Gauge-Factor Compliant Strain Sensor for Machine Learning-Based Sleep Apnea Detection and Prediction
Bibliographic record
Abstract
This paper presents a methodology for fabricating a strain sensor designed explicitly for respiratory monitoring. The sensor’s high gauge factor and low stiffness make it particularly suitable for application to patients who cannot tolerate high stress on their chest, such as infants or the elderly. The sensor comprises a flexible polymeric spring coated with a carbon-based nanocomposite acting as the active layer. Different weight percentages of carbon-based materials are used to determine the percolation threshold, and the percolation threshold was determined to be at 4.75 and 4.25 wt% of graphene and MWCNTs with maximum gauge factors of 949.02 and 117.52, respectively. SEM and gauge factor analysis showed that a higher loading would lead to a more brittle sensor and premature failure due to the contrast in mechanical properties between the active layer and the flexible substrate. A cyclic fatigue test was done on the strain sensor under normal operating conditions. The sensor was able to withstand a total of 21600 cycles without failure with some relatively consistent signal. Additionally, we study the effectiveness of utilizing respiratory signals for sleep apnea event prediction using Short-Time Fourier transforms and 2D Convolutional Neural Networks, a suitable use case for fabricated sensors. The results provide a 1-3% improvement in accuracy over traditional baselines that utilize raw signal data.
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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 | 0.000 | 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 | 0.001 | 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".