Medical biometrics in mobile health monitoring
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
Abstract This work investigates the feasibility of ECG‐based identity management in mobile health monitoring applications. A body area network that operates in conjunction with ECG biometric recognition is explored for mobile monitoring of patients, rescuers, pilots, soldiers, or field agents in general. Among the major challenges of this technology is the stability of the signals over the monitoring duration. Time dependency is responsible for ECG destabilization, which becomes a significant issue for reliable monitoring. We propose a novel framework that addresses this inadequacy, by updating a gallery template when feature matching is compromised. In addition, strategies for tackling privacy issues in medical data management are proposed. A protocol level solution is discussed, to deal with the ethical issues of this technology. An automatic way of aggregating and managing personal information is presented, designated to operate on the basis of anonymity. The experimental performance measured over long‐ECG recordings demonstrates promising results. Copyright © 2010 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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