Bibliographic record
Abstract
Advances in Wireless Body Area Network (WBAN) will allow a range of medical applications that will significantly improve the quality of health care. Placing a number of tiny wireless sensors, on the human body, create a wireless body area network that can monitor various vital signs, providing feedback to the user and medical personnel, a thing that promise to revolutionize health monitoring. Nevertheless the potential of using a body area network with several sensors to monitor vital functions of a human body can only be tapped if we achieve the ease of use and the ease of configuration. In this paper we propose a service-oriented middleware design for WBAN middleware. In the proposed architecture, sensors are coordinated by a gateway node, which in turn retransmits data to a remote central unit and receives WBAN control information and queries from this central unit. The central unit on the other hand will be in charge of storing sensors data, sensor reconfiguration and resource management client, detecting alarms and sending the patients' information to the medical staff. The target user is a patient who needs regular monitoring. The patient usually resides in a care unit or residence for elder people. WBAN in this case shall increase patient comfort and reduces periodical checkups allowing remote monitoring. We believe that the use of Web Services and standardizing the messages exchanged is a potential solution for interoperability and ease of use and configuration challenges. This will attract a larger pool of application developers, leading to more innovative applications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".