MétaCan
Menu
Back to cohort

Smart Bedsheet for Baby Monitoring Application: Measurement and Characterization Results

2020· article· en· W3082308259 on OpenAlexaff
Samal Munidasa, Parastoo Baghaei, Edward Shim, Olivia Lin, Ebrahim Ghafar‐Zadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsYork University
Fundersnot available
KeywordsPotentiostatElectrical conductorCharacterization (materials science)Electrical impedanceMaterials scienceComputer scienceDielectric spectroscopyIntersection (aeronautics)Interface (matter)Electronic engineeringElectrical engineeringComposite materialEngineeringNanotechnologyElectrode

Abstract

fetched live from OpenAlex

This paper describes a research collaboration with Studio 1 Labs to provide the characterization for a novel smart baby monitoring device which includes conductive fabrics. The electrical characterization of the conductive fabrics is important for designing a bedsheet that can adequately be sensitive to physiological movement. Electrical impedance spectroscopy (EIS) has been performed using the Metrohm Autolab potentiostat on a two-fabric interface. For an increase in applied weight, there was an overall decrease in impedance shown both in its real and imaginary components. A simple RC circuit model could be used to describe the system. A test bedsheet was made from a 3x3 conductive fabric matrix stitched into a cotton sheet. Conversely, an increase in resistance was observed from an increase in applied weights at the intersection points of the bedsheet. The following characterization provided useful insight into the future design of the smart bedsheet.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.040
GPT teacher head0.227
Teacher spread0.187 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Sensor and Energy Harvesting MaterialsFrench-language works237,207