Ubiquitous Tele-health System for Elderly Patients with Alzheimer's
Bibliographic record
Abstract
Chronic diseases are becoming one of the world's leading causes of death and disability. By 2020, it is predicted to account for almost three quarters of all deaths. A potential approach to monitor patients with chronic diseases is the implementation of home healthcare systems, using new technologies. The Internet of Things (IoT) and the Radio Frequency Identification (RFID) technologies are evolving rapidly with innovative applications, particularly in the healthcare sector. The aim of this paper is to develop a Tele-health system, based on IoT technology, for monitoring elderly individuals suffering from Alzheimer's. This paper describes a working prototype that is able to capture the vital signs and deliver the desired data care remotely for elderly patients staying at home, using wearable ECG wireless sensor. A UHF passive wearable RFID wristband is used to monitor the whereabouts of the elderly. This prototype is successfully tested on a number of patients at the King Fahd University of Petroleum and Minerals (KFUPM) Medical Centre in Saudi Arabia.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".