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Record W3016629753 · doi:10.1109/sitis.2019.00073

Wireless Body Area Network Based on RFID System for Healthcare Monitoring: Progress and Architectures

2019· article· en· W3016629753 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsBody area networkComputer scienceWirelessRadio-frequency identificationTask (project management)Wireless sensor networkEmbedded systemIdentification (biology)TelecommunicationsComputer networkSystems engineeringEngineeringComputer security

Abstract

fetched live from OpenAlex

Radio Frequency Identification (RFID) technology and Wireless body area network (WBAN) represent the two most strike evolutions for information and communication technology that have attracted attention of researchers and engineers in recent years because they involves several scientific fields. Due to numerous works exploiting the physical integration of RFID devices and WBAN in the healthcare applications, selecting the requirements needed to achieve efficient integrating system is being a challenging task. In this paper we discuss the stat of the art of the matching between RFID technology and WBAN system for healthcare monitoring area and we describes different recent architectures used for this integrating technologies, also a discussion of technical challenges of integrating WBAN and RFID is presented. Finally we propose our suggested RFID body sensor tag design placed directly on human skin for WBAN.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.223
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations18
Published2019
Admission routes1
Has abstractyes

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