Highly survivable bed pressure mat remote patient monitoring system for mHealth
Bibliographic record
Abstract
The high speed mobile networks like 4G and beyond are making a ubiquitous remote patient monitoring (RPM) system using multiple sensors and wireless sensor networks a realistic possibility. The high speed wireless RPM system will be an integral part of the mobile health (mHealth) paradigm reducing cost and providing better service to the patients. While the high speed wireless RPM system will allow clinicians to monitor various chronic and acute medical conditions, the reliability of such system will depend on the network Quality of Service (QoS). The RPM system needs to be resilient to temporary reduced network QoS. This paper presents a highly survivable bed pressure mat RPM system design using an adaptive information content management methodology for the monitored sensor data. The proposed design improves the resiliency of the RPM system under adverse network conditions like congestion and/or temporary loss of connectivity. It also shows how the proposed RPM system can reduce the information rate and correspondingly reduce the data transfer rate by a factor of 5.5 and 144 to address temporary network congestion. The RPM system data rate reduction results in a lower specificity and sensitivity for the features being monitored but increases the survivability of the system from 1 second to 2.4 minutes making it highly robust.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".