Pregnancy Health Monitoring System based on Biosignal Analysis
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
Pregnancy is a special condition in which women go through various health complications throughout the period of gestation. It is not feasible to predict these complications in an absolute manner. Different complications have varying probabilities of occurrence depending upon the phase in pregnancy. Consequently, the only way to ensure a healthy pregnancy is periodic health checkups and continuous health monitoring. Existing literature suggests that conventional health monitoring systems are either too specific or too general, therefore too inflexible to be suited for pregnant women. This paper proposes an end to end solution for tracking the health parameters of users by performing photoplethysmogram (PPG) analysis. Heart Rate Variability (HRV) parameters are calculated and matched with the suggested normal range. The system is capable of distinguishing abnormal HRV readings as a known medical complication specific to pregnancy. This system is available to the end user in the form of a web based application. An important feature of this application is patient-doctor communication. Once, any complication is diagnosed, an alert is generated wherein the user has an option of sharing the report of their diagnosis with their caretakers.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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