MCEP: A Mobile Device Based Complex Event Processing System for Remote Healthcare
Bibliographic record
Abstract
This paper introduces an edge-computing based Complex Event Processing (CEP) architecture for Remote Patient Monitoring (RPM) which is an important issue in the context of remote healthcare. In this architecture, the detection of complex events, that may indicate impending health problems, is performed on a mobile device that receives data from sensors attached to the body of a patient. The detected complex events are sent to a back-end hospital server running on a cloud for further processing. Current state-of-the-art RPM techniques use the mobile device as an IoT gateway agent to forward data streams from health sensors to a remote hospital server where complex events are detected. A drawback of this existing methodology is that the mobile phone always needs to remain connected to the hospital server. Also, the mobile network consumption is increased while transferring large volumes of sensor data streams thus leading to an increase in the user cost. Additionally, it leads to an increase in the workload at the hospital server that serves multiple patients. This research investigates a mobile device based CEP system for addressing these issues and demonstrates its viability through a proof-of-concept prototype. A thorough performance analysis is performed using a synthetic workload that provides insights into system scalability and the relationship between system/workload parameters and performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".