Remote monitoring of vital signs in older adults for prevention of cognitive decline
Bibliographic record
Abstract
Nowadays the prevention of dementia is a challenge for humanity. There are some preventive intervention programs for dementia, which are mainly based in the modification of multicomponent lifestyles such as: physical and cognitive activity, weight control, metabolic-comorbidity control and social support. Recently, Mind and Movement Program to have Cognitive Health is a collaborative methodological proposal between the countries Mexico, Japan and Canada, which consists of three components: aerobic exercise; aerobic and cognitive exercises, as well as a motivation program. For performing aerobic and cognitive exercises, the monitoring of vital signs in real time is necessary through a statistical analysis of the data of each patient, in such a way that the doctor knows the state of health of the patient. As a consequence of the COVID-19 pandemic, the original program to acquire experimental data underwent modifications. Since the older adults were isolated, they were required to do their physical exercises at home, implementing a remote monitoring system based on a wearable smart band, which was properly developed to monitor the vital signs for each patient. Hence, a personalized quantification of the oxygen saturation and cardiac pressure based on light sensors and pressure sensors, respectively, was measured and monitored in real time. On the other hand, predefined programming based on Artificial intelligence, provides certain advantages for easy handling by the older adults. Currently, we are working along with a hospital, where doctors involved in the program are testing the prototype for the validation of the wearable smart bands.
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.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".