Validation of wearable vital signs monitoring: A comparison with conventional bedside patient monitors
Bibliographic record
Abstract
Objective: This study aimed to assess how accurately mobile wearable devices could be used to monitor vital signs continuously in clinical settings by comparing their measurements with those from traditional bedside monitors. Methods: Data collected from Mindray's mWear wearable device were compared against measurements from the BeneVision N15 traditional bedside monitoring system. A total of 208 paired datasets, including blood pressure, oxygen saturation, heart rate, and respiratory rate, were collected from 16 healthy volunteers in a clinical setting. Bland-Altman analysis was applied to assess agreement between the two devices. Results: The analysis showed that 94.2% of the data variance points for oxygen saturation, diastolic blood pressure, and pulse rate fell within the limits of agreement. For systolic blood pressure, 92.3% of the data variance points were within limits, while 94.7% of the heart rate and respiratory rate data points were also within agreement limits. Conclusion: There was a strong agreement between the wearable mWear device and the traditional bedside patient monitoring system. This study supports the accuracy and reliability of wearable devices for continuous vital signs monitoring. These results encourage the wider use and ongoing improvement of wearable technology in clinical practice.
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".