A Multi-Layered Edge-Secured Cloud Framework for Healthcare Monitoring in Old-Age Homes Using Smart Systems Driven by Comprehensive User Interaction
Bibliographic record
Abstract
Most chronic diseases and health disorders at early stages are either asymptomatic or have long latency periods, especially among the older age groups. This eventually becomes difficult to handle at later stages and in some cases even becomes the cause of death. Even though cloud-based smart systems have delivered promising results in the field of healthcare monitoring, due to high latency, network congestion, poor user control, etc. smart systems fail to meet the required needs. Especially in old-age homes, where 24/7 service is required, healthcare facilities need robust, prompt, and real-time smart systems. This paper proposes a multi-layered cloud-edge architecture for old-age homes that operates on smart systems to detect anomalies, predict future risks, and process data across the doctor, patient, and the cloud in real-time. The device operates mostly on the edge layers to process patient information using neural networks and send alerts to the end-user based on predictions and anomalies before sending the aggregated data to the cloud. The proposed framework includes smart devices which are facilitated with responsive user interfaces which keep the patients and doctors in complete control of the smart system while maintaining efficient data communication and security. This paper also compares the latency delay between IoT devices and cloud, also between IoT devices and edge devices.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".