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Record W2127229009 · doi:10.1109/apmc.2009.5384514

Integrated radar systems for precision monitoring of heartbeat and respiratory status

2009· article· en· W2127229009 on OpenAlexafffund
Lydia Chioukh, Halim Boutayeb, Lin Li, L’Hocine Yahia, Ke Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsHeartbeatDoppler radarRadarSensitivity (control systems)Computer scienceElectronic engineeringMicrowaveExtremely high frequencyPulse-Doppler radarContinuous-wave radarSIGNAL (programming language)Radar engineering detailsSignal processingDoppler effectReal-time computingAcousticsEngineeringTelecommunicationsRadar imagingPhysics

Abstract

fetched live from OpenAlex

This work presents simulation and measurement results of the vital sign parameters based on low-power microwave and millimeter-wave monitoring systems through Doppler radar techniques. The cardiac beating and the breathing of patients are examined. Three systems operating at 5.8 GHz, 24 GHz and 35 GHz, respectively, are designed, simulated, and fabricated. Using such three systems and applying signal processing techniques, measured signals obtained at distance up to 1 m from the patient of reference are presented. These results show that the heartbeat and the frequency of breath are well detected which validates our radar analysis and design approach. Performances (sensitivity, complexity, etc.) of the different systems are compared and studied, showing that the highest sensitivity detection can be achieved with the system at the highest frequency (35 GHz) in this case.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.253
Teacher spread0.230 · 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 designSimulation or modeling
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

Citations31
Published2009
Admission routes2
Has abstractyes

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