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Oral Cavity Pressure Measurement-based Respiratory Monitoring System with Reduced Susceptibility to Motion Artifacts

2020· article· en· W3082127199 on OpenAlexaff
Seyedfakhreddin Nabavi, Sharmistha Bhadra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsMcGill University
Fundersnot available
KeywordsOral cavityBreathingPressure measurementSystem of measurementWearable computerRespirationPressure sensorComputer scienceRespiratory monitoringBiomedical engineeringAcousticsRespiratory systemSimulationMedicineAnesthesiaEngineeringPhysicsEmbedded systemDentistryInternal medicine

Abstract

fetched live from OpenAlex

In this paper, we propose a novel approach for respiratory monitoring through the direct measurement of oral cavity pressure. To measure the oral cavity pressure, a pressure sensor is placed inside the oral cavity. The intraorally obtained pressure signals are analyzed in the time-domain and validated against the conventional respiration monitoring belt (reference measurement). Tests have been performed on four subjects (four tests on each subject) in stationary and non-stationary conditions to evaluate the usage of the system in real life. Measurement from the proposed system shows that our approach can monitor the respiration rate with an accuracy of 99% when compared to the reference measurement. Moreover, the system can effectively track the respiration pattern and can detect breathing events independent of breathing routes, i.e., the nasal and oral. It has the minimum susceptibility to motion artifacts. Therefore, it has potential to be used as a wearable monitoring system for day to day life.

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 categoriesMeta-epidemiology (narrow)
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 score0.119
Threshold uncertainty score1.000

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 head0.057
GPT teacher head0.236
Teacher spread0.179 · 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.

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
Published2020
Admission routes1
Has abstractyes

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