AI-Enhanced Wearable Sensors Connected to Cloud Platforms for Continuous Health Monitoring in Aging Populations
Bibliographic record
Abstract
As the world's population ages, there is an increasing demand for new ways for maintaining an eye on wellness all the time. This paper introduces a comprehensive framework that integrates AI-enhanced mobile sensors and cloud-connected services for real-time and non-invasive health monitoring of elderly people. The system uses the latest machine learning technologies in wearables to smartly decode cardio-respiratory data like heart rate, blood pressure, blood oxygen levels, and mobility features. With cloud infrastructure, you can store data when you need it, process it from a distance, and get feedback that is specific to you. This lets doctors act quickly and makes things easier for healthcare systems. The developed architecture ensures data privacy, interoperability, and energy efficiency, providing a solid basis for advanced geriatric healthcare. Prototyping and experimental outcomes illustrate the efficacy of this methodology in enabling early anomaly detection and predicting future health trends, signalling a new epoch of elderly care underpinned by continuous monitoring driven by artificial intelligence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".