Intelligent In-House Monitoring for the Elderly
Bibliographic record
Abstract
Heat emergencies such as heatstroke, high fever and occupational heat exposure are all serious medical conditions that require emergency response. These are of particular concern for the elderly or infirmed, who may be home alone and unable to summon help. We present a paper on an Intelligent In-House Monitoring system for the elderly, designed to collect both physiological and environmental data in order to determine if an individual is experiencing a heat emergency. This system was designed to non-intrusively and non-invasively measure pulse rate, perspiration and body temperature to determine whether or not a person is experiencing some sort of heat emergency. The monitoring system is comprised of a wearable monitor that transmits the information wirelessly to a base station that records both ambient air temperature and humidity. The base station combines the data from the monitor with the environmental data to determine if the wearer is experiencing a heat emergency. If it determines that a heat emergency is occurring then it will attempt to reach an emergency contact, as well as warn the wearer of the need to cool down .
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".