Smart Device for Monitoring Persons with Alzheimer Disease
Bibliographic record
Abstract
Alzheimer's disease, affecting millions globally, presents significant health challenges that require specialized attention. Continuous medical follow-up is crucial for early diagnosis, effective symptom management, and providing necessary support to patients and their families. In the biomedical field, innovative systems like the “Kiwatch” video surveillance camera and the “ELLiQ” robot have been developed to monitor individuals with Alzheimer's. However, these solutions have notable limitations, particularly in real-time tracking of patient location and physiological parameters. Currently, no device on the market combines both functions. To address these gaps, this paper proposes a smart device for the remote monitoring of Alzheimer's patients, focusing on key metrics such as location tracking, body temperature, and heart rate. We developed a smart portable system, a functional prototype integrating an Arduino Mega board, a temperature sensor, a heartbeat sensor, and a Bluetooth module. This setup enables the transmission of vital health information to a smartphone via the Bluetooth Terminal application. Rigorous testing was conducted to evaluate the prototype's effectiveness in the remote monitoring of Alzheimer's patients
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".