In-Ear Audio Wearable: Measurement of Heart and Breathing Rates for Health and Safety Monitoring
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: This paper examines the integration of a noninvasive vital sign monitoring feature into the workers' hearing protection devices (HPDs) by using a microphone positioned within the earcanal under the HPD. METHODS: 25 test-subjects were asked to breathe at various rhythms and intensities and these realistic sound events were recorded in the earcanal. Digital signal processing algorithms were then developed to assess heart and breathing rates. Finally, to test the robustness of theses algorithms in noisy work environments, industrial noise was added to the in-ear recorded signals and an adaptive denoising filter was used. RESULTS: The developed algorithms show an absolute mean error of 4.3 beats per minute (BPM) and 2.7 cycles per minute (CPM). The mean difference estimate is -0.44 BPM with a limit of agreement (LoA) interval of -14.3 to 13.4 BPM and 2.40 CPM with a LoA interval of -2.62 to 7.48 CPM. Excellent denoising is achieved with the adaptive filter, able to cope with ambient sound pressure levels of up to 110 dB SPL, resulting in a small error for heart rate detection, but a much larger error for breathing rate detection. CONCLUSION: Extraction of the heart and breathing rates from an acoustical measurement in the occluded earcanal under an HPD is possible and can even be conducted in the presence of a high level of ambient noise. SIGNIFICANCE: This proof of concept enables the development of a wide range of noninvasive health and safety monitoring audio wearables for industrial workplaces and life-critical applications where HPDs are used.
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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