Heterogeneous Multi-Hop Transmission of Compressed ECG Data from Wireless Body Area Network
Bibliographic record
Abstract
Nowadays, the healthcare market keeps growing with an increasing aging population. As a promising technology, a body area network (BAN) consisting of biomedical sensors across a human body can gather vital life signals such as electrocardiogram (ECG), pulse, and blood pressure to facilitate effective diagnosis. The BAN can be further integrated with existing wireless infrastructure to offer mHealth services. In this paper, we analyze the transmission performance of compressed ECG data over a heterogeneous multi-hop wireless channel. The ECG data from a BAN are compressed and sent through a Bluetooth-enabled ECG monitor to a smart phone and thereafter to a cellular base station. Due to potentially life-threatening situations, timely delivery of ECG data is an essential requirement. Exploiting the inherent heartbeat pattern in ECG traffic, we introduce a context-aware packetization for ECG transmission. Further, a non-preemptive priority rule is applied to mitigate the impact of background traffic and prioritize the transmission of critical ECG data. Then, we analytically evaluate the overall transmission delay of ECG packets.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".