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Record W1984388534 · doi:10.1109/iembs.2010.5627525

A contact-free respiration monitor for smart bed and ambulatory monitoring applications

2010· article· en· W1984388534 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsUniversity of TorontoCentre for Social InnovationUniversity Health NetworkMcGill University
FundersMcGill University
KeywordsCapacitive sensingVolunteerCapacitanceExpirationMaterials scienceBiomedical engineeringComputer scienceRespiratory systemMedicineElectrodeOperating system

Abstract

fetched live from OpenAlex

The development of a contact-free respiration monitor has a broad range of clinical applications in the home and hospital setting. Current approaches suffer from a variety of problems including unreliability, low sensitivity, and high cost. This work describes a novel approach to contact-free respiration monitoring that addresses these shortcomings by employing a highly sensitive capacitance sensor to detect variations in capacitive coupling caused by breathing. A prototype system consisting of a synthetic-metallic pad, sensor electronics, and iPhone interface was built and its performance compared experimentally to the gold standard technique (Respiratory Inductance Plethysmography) on both a healthy volunteer and SimMan robotic mannequin. The prototype sensor effectively captured respiratory movements over breathing rates of 5-55 bpm; achieving an average spectral correlation of 0.88 (CI: 0.86-0.90) and 0.95 (CI: 0.95-0.96) to the gold standard using the SimMan and healthy volunteer respectively.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.693

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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
Published2010
Admission routes2
Has abstractyes

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