A Novel Modular Framework for Secure and Scalable Remote Health Monitoring: RHMS
Bibliographic record
Abstract
Background: Remote health monitoring for time-critical conditions (e.g., acute stroke) demands rapid, reliable data delivery and immediate clinical interpretation. However, existing Remote Patient Monitoring (RPM) frameworks often exhibit fragmented designs, latency bottlenecks, and integration challenges when onboarding new sensors or clinical algorithms. Methods: To address these gaps, we introduce a unified Remote Health Monitoring System (RHMS) that combines MQTT-driven sensor transport, a pattern-oriented software architecture, and blockchain-based immutable audit logging. Results: In a TRL 3–4 technical feasibility evaluation using synthetic load and a 30 min smartwatch trace, RHMS achieved a median end-to-end latency of 480 ms (IQR 110 ms; P95 < 600 ms) under 500 concurrent 1 Hz streams and a peak throughput of 545 streams/s in controlled environments. The system emits algorithmic risk alerts from an integrated model; no adjudicated clinical diagnoses were performed. A modeled rollup-backed audit log estimates a per-record cost of $0.00016 (USD). Conclusion: RHMS demonstrates technical feasibility and interoperability that aligns with clinical recommendations. Clinical validation is out of scope for this study and will require prospective trials.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".