Secure Telemedicine: Biometrics for Remote and Continuous Patient Verification
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 technological advancements in the field of remote sensing have resulted in substantial growth of the telemedicine industry. While health care practitioners may now monitor their patients’ well-being from a distance and deliver their services remotely, the lack of physical presence introduces security risks, primarily with regard to the identity of the involved parties. The sensing apparatus, that a patient may employ at home, collects and transmits vital signals to medical centres which respond with treatment decisions despite the lack of solid authentication of the transmitter’s identity. In essence, remote monitoring increases the risks of identity fraud in health care. This paper proposes a biometric identification solution suitable for continuous monitoring environments. The system uses the electrocardiogram (ECG) signal in order to extract unique characteristics which allow to discriminate users. In security, ECG falls under the category of medical biometrics , a relatively young but promising field of biometric security solutions. In this work, the authors investigate the idiosyncratic properties of home telemonitoring that may affect the ECG signal and compromise security. The effects of psychological changes on the ECG waveform are taken into consideration for the design of a robust biometric system that can identify users based on cardiac signals despite physical or emotional variations.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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