IoT-Based COVID-19 Health Monitoring System: Context, Early Warning and Self-Adaptation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Internet of Things (IoT) has enabled novel solutions for monitoring patients’ health through wearable sensors in conditions of both non-communicable and infectious diseases. In this paper, we report work in progress involving the development of an IoT-based COVID-19 health monitoring system that can effectively monitor the essential physiological functions of a patient through wireless sensors, thus supporting the early detection of severe cases and the continuous assessment of the patient status. The work provides several main contributions, as it includes: (i) a brief description of the current IoT-based system for remote monitoring of COVID-19 patients; (ii) a description of embedded characteristics of our device, including its contextual functions, early warning score mechanisms and self-adaptive features; and (iii) a description of our preliminary experiment results. Our proposed solution reduced drastically the amount of redundancy in data and still maintain monitoring accuracy. Given the COVID-19 scenarios, in which human resources are extended to the limit and the number of patients in severe conditions is often high, a system that can support IoT-based continuous monitoring are essential to identify changes in clinical status promptly and accurately and can potentially transform the way patients are monitored.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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