Design and Development of a Wristband for Continuous Vital Signs Monitoring of COVID-19 Patients
Bibliographic record
Abstract
The novel coronavirus disease (COVID-19), as a pandemic, has intensely impacted the global healthcare systems. Remote health monitoring of positive COVID-19 patients isolating at home has been identified as a practical approach to minimize the mortality rate. This work proposes a cost-effective and ease-to-use wristband with the capability of continuous real-time monitoring of heart rate (HR), respiration rate (RR), and blood oxygen saturation (SpO2), temperature and accelerometry. The proposed wristband comprises three different sensing elements, namely, PPG sensor, temperature sensor, and accelerometer. The sensors' output signals are transmitted via Bluetooth. Process of the PPG signals measured from the wrist anatomical position provides essential information regarding HR, RR, and SpO2. The deployed temperature sensor and accelerometer, measure the wearers’ body temperature and physical activities. Experimental results obtained from a group of subjects demonstrate that the wristband can monitor HR, RR, SpO2, and body temperature with the Mean Absolute Errors (MAEs) of 2.75 bpm, 1.25 breaths/min, 0.64%, and 0.22 Co, respectively. Such a small variation confirms that the wristband can be potentially deployed in the public health network to determine and track patients infected by COVID-19.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".