Operating system performance measurements for Remote Patient Monitoring applications
Bibliographic record
Abstract
The Remote Patient Monitoring (RPM) is becoming vital part of healthcare improving quality of care. The RPM system uses variety of sensors and wireless technologies to monitor multiple biological and environmental signals simultaneously providing status and trend data for the patient. The RPM system can also provide alarms/alerts for the patient or the caregiver in real-time so that the patient gets assistance in timely manner when an acute event occurs. The RPM system must detect such events in real-time to generate alarms/alerts. Use of mobile devices like smartphones and/or tablets for RPM enables patient mobility and provides real-time monitoring capability. The mobile device Operating System (OS) used for real-time RPM needs to meet the hard real-time requirements for alerts/alarms generation. The General Purpose OS (GPOS) uses fair scheduling algorithm for multitasking while Real Time OS (RTOS) uses preemptive scheduling. This paper evaluates the real-time performance of GPOS and a RTOS (QNX) under variety of load condition for RPM application. The results of the measurements indicate that the mobile device OS used for RPM must provide a prioritizing mechanism to satisfy the hard real-time requirements when the mobile device is multitasking and/or overloaded.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".