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Validation Of A Novel Respiratory Monitoring Method and System Based on Antenna Sensors and Optical Tracking of Chest Motion

2024· article· en· W4405489361 on OpenAlexfundno aff
Mehran Ahadi, Amine Miled, Marc-André Dugas, Younès Messaddeq

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsnot available
FundersCanada First Research Excellence Fund
KeywordsTracking (education)Respiratory monitoringComputer scienceAntenna (radio)Computer visionMotion (physics)Motion sensorsTracking systemArtificial intelligenceRespiratory systemMedicineTelecommunicationsInternal medicine

Abstract

fetched live from OpenAlex

The effectiveness of a wearable sinusoidal dipole antenna sensor in monitoring respiratory activity is investigated. An experimental setup combining the antenna sensor and a highly accurate optical tracking system is utilized, and a strong correlation between the antenna's reflection signal and the optical tracker is demonstrated during various respiratory behaviors. Obtained results show the antenna sensor's high accuracy in detecting respiratory cycles, amplitudes, and identifying sleep apnea events. The maximum mean squared error in cycle timings is calculated as 0.0044 seconds squared, and the maximum relative error of measured amplitudes is calculated as ±3.95%. Overall, these findings underscore the potential of sinusoidal antenna sensors as an effective tool for respiratory monitoring.

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 score0.315
Threshold uncertainty score0.489

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 head0.038
GPT teacher head0.279
Teacher spread0.241 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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