Technology for continuous long-term monitoring of pregnant women for safe childbirth
Bibliographic record
Abstract
This research explores the Internet of Things and how it can be used to improve patient monitoring in modern healthcare to ensure the safety of pregnant women and their children. The concept of Internet of Things and connected healthcare will be put into context regarding improving the outcome of pregnancy for women with limited access to health care. Through our ongoing research project, we propose a system that can positively impact the standard of life for pregnant mothers. Modern day smartphones have proven to be extremely pervasive in developing regions of the world. Most of these smartphones are equipped with hardware that can support biometric monitoring of its user. By taking advantage of the sensors present on smartphones and an accessory biomedical signal acquisition device we intend to unobtrusively acquire vital information about the health status of pregnant women. The acquired data is then processed and classified using signal processing and analysis tools to assist healthcare practitioners evaluate the status of pregnant women remotely. Since most pregnant women in our area of focus already own smartphones, the cost associated with our system is minimal. We have successfully implemented remote heart rate monitoring and physical activity monitoring using a smartphone and an accessory device.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".