Fetal Kicking Monitoring Device for Intrauterine Death Prevention
Bibliographic record
Abstract
Background: The fetal health is possible to fluctuate and deteriorate and lead to unexpected loss of the pregnancy. Time is crucial for heart life and any decrease in oxygen to the heart muscle is crucial and means death. Therefore, it is substantial to do an obstetric tracing, in order to spot the sudden changes in the fetus health. Problem Statement: Away from all the methods that could measure heart health, fetus movements monitoring is one way to identify the fetal wellbeing. One very popular movement that is used to gauge fetal health is a fetal kick, in which a frequency of perceived and/or registered fetal kicks by a healthy fetus is higher as compared to the frequency of perceived and/or registered fetal kicks a by an unhealthy fetus. However, the conventional methods such as ultrasound and manual measurement endure some errors. Objective: The aim of this study is to develop a portable belt that can be used to measure the fetal movement accurately by setting the appropriate threshold. Methods: A total of 9 Force Sensitive Resistors (FSR) were used to detect a simulated force exerted by fetus on the abdomen of pregnant women in order to count the fetal movements. Finding: Based on the overall result the sensor detects 90% of the kicks given. Conclusion: We believe that this device could help the pregnant women to measure the fetal movement with less attention and can reduce the error. Keywords: Fetal Health and Technology, Kicks, Monitoring, Sensor
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".