A Platform for e-Health Control and Location Services for Wandering Patients
Bibliographic record
Abstract
Wandering patients frequently have diseases that demand continuous health control, such as taking pills at specific times, constant blood pressure and heart rate monitoring, temperature and stress level checkups, and so on. These could be jeopardized by their wandering behavior. Mobile applications that focus on health care have received special interest from medical specialists. These applications have been widely accepted, due to the availability of smart devices that include sensors. However, sensor-based applications are highly energy demanding and as such, they can be unaffordable in mobile e-health control due to battery constraints. This paper presents the design and implementation of a platform aimed at providing support in e-health control and provision of location services for wandering patients through real-time medical and mobility information analysis. The platform includes a configurable mobile application for heart rate and stress level monitoring based on Bluetooth Low Energy technology (BLE), and a web service for monitoring and control of the wandering patients. Due to battery limitations of smart devices with sensors, the mobile application includes energy-efficient handling and transmission policies to make more efficient the transmission of medical information from the sensor-based smart device to the web service. In turn, the web service provides e-health control services for patients and caregivers. Through the platform functionality, caregivers (and patients) can receive notifications and suggestions in response to emergency, contingency situations, or deviations from health and mobility patterns of the wandering patients. This paper describes a platform that conceals continuous monitoring with energy-efficient applications in favor of e-health control of wandering patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".