Wearable Wireless-Enabled Oscillometric Sphygmomanometer: A Flexible Ambulatory Tool for Blood Pressure Estimation
Bibliographic record
Abstract
This article presents the design of an unobtrusive and wireless-enabled blood pressure (BP) monitoring system that is suitable for ambulatory use. By adopting low-profile electromechanical actuators and a compact printed circuit board design, this lightweight device can be worn directly on the occlusive cuff, therefore eliminating the need of a long and obtrusive tubing interconnect between the device and the cuff, as seen in traditional ambulatory BP monitors (ABPM). Instead of executing the BP estimation algorithm directly on the device, the proposed design rather sends the raw oscillometric signal through a Bluetooth Low Energy link, thus granting any Bluetooth-enabled device to gather and process the signal using a dedicated application. This in turn allows to assess several BP estimation algorithms found in the literature without being limited by the device resources. Three of them were tested with the designed prototype and validated with a reference equipment on 11 subjects. Overall, two of the algorithms revealed a mean absolute difference with the reference equipment of less than 5 mmHg and almost zero bias along with a standard deviation of less than 6 mmHg. Reproducibility results shown a mean difference between successive measurements of less than 3.1 mmHg and a standard deviation of less than 2.4 mmHg. The assembled prototype dimensions are 63.8 × 134.8 × 24.8 mm and features an autonomy of 63.1 hours. Comparison with commercial ABPM devices shown that the proposed design is 18% to 33% smaller volume-wise, 5% to 27% weight-wise and height is reduced by 17% to 25%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".