Blood Pressure Measurement and Management Telemedicine System Based on a Smart-Phone
Bibliographic record
Abstract
Variation of blood pressure throughout the day is one of the reasons why it is increasingly evident that the traditional way of measuring blood pressure in the clinic or office frequently produces numbers that grossly overestimate patientâ??s true blood pressure level. This is a major problem, since it is one of the most important and frequent measurements made by physicians. High blood pressure (BP) (hypertension) is a leading chronic condition in the globe and a major risk factor for severe diseases. Measuring the BP as accurately as possible can save a lot of human lives. However, the measurement and management platform can still be improved. In this paper, we describe a complete low-cost prototype system that we have developed for this purpose that can be part of and m-Health platform. Our BP telemedicine device is based on the oscillometric method for measuring BP. A microcontroller oversees measurement operations, to process acquired readings, and to calculate the heart-rate. A smart mobile phone commands the operation of our developed system via Bluetooth. A custom Windows Mobile application software was designed and developed to command the operation of this platform and to receive these vital measurements in a convenient manner. Obtained measurements can be stored on the mobile device to form a local database or transmitted via a designated wireless protocol.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".