Inertial and Flexible Resistive Sensor Data Fusion for Wearable Breath Recognition
Bibliographic record
Abstract
This paper proposes a novel data fusion technique for a wearable multi-sensory patch that integrates an accelerometer and a flexible resistive pressure sensor to accurately capture breathing patterns. It utilizes an accelerometer to detect breathing-related diaphragmatic motion and other body movements, and a flex sensor for muscle stretch detection. The proposed sensor data fusion technique combines inertial and pressure sensors to eliminate nonbreathing body motion-related artifacts, ensuring that the filtered signal exclusively conveys information pertaining to breathing. The fusion technique mitigates the limitations of relying solely on one sensor’s data, providing a more robust and reliable solution for continuous breath monitoring in clinical and home environments. The sensing system was tested against gold-standard spirometry data from multiple participants for various breathing patterns. Experimental results demonstrate the effectiveness of the proposed approach in accurately monitoring breathing rates, even in the presence of nonbreathing-related body motion. The results also demonstrate that the multi-sensor patch presented in this paper can accurately distinguish between varying breathing patterns both at rest and during body movements.
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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 | 0.000 | 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".