Technology for continuous long-term monitoring of pregnant women for safe childbirth
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
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.
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