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Record W2045628585 · doi:10.1109/bibe.2014.67

Design of an e-Health Respiration and Body Posture Monitoring System and Its Application for Rib Cage and Abdomen Synchrony Analysis

2014· article· en· W2045628585 on OpenAlexaff
Atena Roshan Fekr, Katarzyna Radecka, Željko Žilić

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsMcGill University
Fundersnot available
KeywordsAccelerometerRib cageBreathingAbdomenRespirationSimulationPhysical medicine and rehabilitationComputer scienceMedicineReal-time computingAnatomy

Abstract

fetched live from OpenAlex

Automated methods of real-time and un-obstructive patient's respiration and position monitoring have been subjects of interests in e-health applications. The present study implements a low-cost and convenient monitoring system for patients with breathing problems or sleep disorders. We have also captured the Rib Cage (RC) and Abdomen (AB) movements using accelerometer sensors. In addition to measurement of phase shift between the chest wall compartments, the impacts of different body positions on AB and RC motions have been investigated. The performance of the presented system is evaluated and the average Mean Square Error (MSE) of 0.14 is achieved for three breath timing variables. Moreover, the overall errors of phase angles for paradoxical and synchronous breath patterns are 0.25°±0.06 and 0.26°±6E-03, respectively. The system properly indicates a significant increase in the degree of ribcage and abdomen asynchrony in the paradoxical breathing compared to normal pattern.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.249
Teacher spread0.236 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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