Pregnancy Health Monitoring System based on Biosignal Analysis
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".