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A Wearable mmWave Radar System for Continuous Cardiopulmonary Monitoring

2023· article· en· W4386525099 on OpenAlexaff
Serene Abu-Sardanah, Omar M. Ramahi, George Shaker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWearable computerRadarWaveformComputer scienceRemote patient monitoringRadar systemsDisplacement (psychology)Radar trackerRadar engineering detailsElectronic engineeringAcousticsReal-time computingRemote sensingElectrical engineeringTelecommunicationsEngineeringRadar imagingPhysicsMedicineEmbedded systemGeology

Abstract

fetched live from OpenAlex

A 60 GHz wearable radar system is introduced for the first time for the continuous monitoring of human respiratory patterns and cardiac waveforms, including the detection of the respiratory rate (RR) and heart rate (HR). Full-wave electromagnetic simulations are presented to investigate the performance of the radar antennas in close proximity to a human subject. A physical prototype was developed, and a measurement campaign shows high accuracy levels for RR and HR detection, with cardiac displacement waveforms being mapped successfully against a standard electrocardiogram (ECG).

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 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 score0.148
Threshold uncertainty score0.909

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 head0.017
GPT teacher head0.223
Teacher spread0.206 · 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 teacher head, 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

Citations0
Published2023
Admission routes1
Has abstractyes

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