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Record W2953550083 · doi:10.1109/lsens.2019.2924695

Noncontact Vital Sensing With a Miniaturized 2.4 GHz Circularly Polarized Doppler Radar

2019· article· en· W2953550083 on OpenAlexaff
Changzhan Gu, Yuchu He, Jiang Zhu

Bibliographic record

VenueIEEE Sensors Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRadarAntenna (radio)Doppler radarTransceiverContinuous-wave radarPhysicsHeartbeatOpticsDoppler effectAcousticsComputer scienceRemote sensingElectronic engineeringTelecommunicationsRadar imagingEngineeringWirelessGeology

Abstract

fetched live from OpenAlex

Conventionally, in Doppler radar vital sign detection, two separate linearly polarized TX and RX antennas are used and often placed more than half wavelength away from each other to mitigate mutual coupling. However, the overall footprint of the radar system with separate TX/RX antennas may make it difficult to be integrated into compact consumer devices. In this article, a single dual circularly polarized circular patch antenna was presented to help to achieve a miniature Doppler radar system. The proposed antenna was designed, integrated, and demonstrated with a 2.4 GHz radar transceiver module, but the design method and approach could be extended to higher frequencies to help to reduce the antenna area needed for radar sensing. The designed antenna at 2.4 GHz has the largest dimension of no larger than 5 cm and TX/RX isolation of better than 30 dB, ensuring good detection sensitivity. With a low output power of 0 dBm, both respiration and heartbeat movements of human test subjects can be detected accurately.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.005
GPT teacher head0.176
Teacher spread0.171 · 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

Citations25
Published2019
Admission routes1
Has abstractyes

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