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Record W4293794926 · doi:10.1109/lmwc.2022.3198174

Body Motion Artifact Cancellation Technique for Cough Detection Using FMCW Radar

2022· article· en· W4293794926 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.

Bibliographic record

VenueIEEE Microwave and Wireless Technology Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsContinuous-wave radarArtifact (error)SIGNAL (programming language)RadarAcousticsComputer scienceBinVibrationFrequency modulationElectronic engineeringComputer visionRadar imagingPhysicsRadio frequencyEngineeringTelecommunicationsAlgorithm

Abstract

fetched live from OpenAlex

A body motion artifact cancellation (BMAC) technique is proposed to detect human cough signals using a frequency-modulated continuous-wave (FMCW) radar. Human coughs are spontaneously accompanied by large-scale body motions, which overwhelm small-scale vibration signals induced by coughing. To mitigate these effects, motion-induced phase variations are estimated and compensated at respective frequencies of an FMCW signal. The cough signals are extracted from range profiles of the phase-compensated FMCW signals. This allows collecting cough signals at a fixed range bin, which are not so much distorted by range migrations due to the large-scale body motions. It is verified through both simulations and experiments. The experimental result shows that it can clearly detect human cough signals by removing body motion artifacts.

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 score0.383
Threshold uncertainty score0.966

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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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