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Record W3035642962 · doi:10.1177/1558925020930354

A knitted wearable flexible sensor for monitoring breathing condition

2020· article· en· W3035642962 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

VenueJournal of Engineered Fibers and Fabrics · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of British Columbia
FundersZhejiang Sci-Tech UniversityZhejiang University
KeywordsBreathingWearable computerAcousticsYarnMaterials scienceSIGNAL (programming language)Electrical conductorRespiratory monitoringComputer scienceBiomedical engineeringStructural engineeringComposite materialEngineeringRespiratory systemEmbedded systemMedicinePhysicsAnatomy

Abstract

fetched live from OpenAlex

This article presents the development of a knitted flexible sensor, which is embedded into a seamless garment, for monitoring health condition. The sensor is designed as an elastic weft-knitted plain structure, where the conductive silver-plating filament yarn is used for breathing signal collection and the spandex core-spun yarn is used to ensure good attachment of the garment to human body. The breathing amplitude and breathing frequency are obtained from the variations in sensor resistance. The sensor can measure various kinds of breathing conditions, such as normal breathing, slow breathing, fast breathing, and shallow breathing. The results are in reasonable agreement with the actual condition. Such kind of flexible sensors have the advantages of wearable and comfort.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.478

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 head0.022
GPT teacher head0.234
Teacher spread0.212 · 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