Context awareness in WBANs: a survey on medical and non-medical applications
Bibliographic record
Abstract
Wireless Body Area Network (WBAN) applications help reduce medical costs and improve people's quality of life by monitoring a user's biological signals via wearable and implantable wireless sensors. In order to satisfy burgeoning requirements, context-aware solutions are needed, thus allowing the system to adapt to changes in the user's mood, mental states, biological signals, and the environment. Thus, contextual information must be measured alongside biological signals in order to characterize and understand the current situation to adapt the system. With these issues in mind, this survey presents an overview of context-aware solutions at the Medium Access Control (MAC) and application layers. A clear distinction between medical and non-medical applications is made and some promising latest commercial WBAN products are highlighted. We show that, despite the importance of context-awareness in WBAN applications, there are limited solutions available, particularly at the MAC layer. The survey concludes with a discussion on open research challenges for future context-aware WBANs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".