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Sensing Probe Development for Pulse Wave and Blood Pressure Detection using Fiber Optic Based Fabry-Perot Interferometer

2023· article· en· W4388449461 on OpenAlexfundno aff
Napatsorn Ratanapanya, Saroj Pullteap

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsnot available
FundersNational Research Council of ThailandRotman Research Institute, Baycrest
KeywordsFabry–Pérot interferometerMaterials scienceInterferometryOpticsOptical fiberInterference (communication)DetectorRepeatabilityDemodulationOptoelectronicsPhysicsTelecommunications

Abstract

fetched live from OpenAlex

A low-cost sensing probe based on fiber optic Fabry-Perot interferometer (FFPI) was designed and developed for pulse wave and blood pressure measurements. The FFPI system was comprised of the light source, sensing probe, and photodetector. To develop the sensing probe, four reflective materials and also three elastic materials were demonstrated to measure the reflectivity and elasticity, respectively. Furthermore, the probe was tested on the fingertip and neck to obtain the interference fringes with five times of repeatability. Besides, the peak detection and fringe counting techniques were employed to demodulate interference fringes into pulse waves. The results revealed that Aluminum-coated mirror provided the highest reflectivity, while a latex rubber balloon, the most elastic material, had a percentage of elongation of 199.25 mm and a Young's modulus of 2.85 MPa. To conclude, the sensing probe had the ability to detect heart pulses, paving the way for an affordable, painless, and user-friendly instrument.

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 score0.278
Threshold uncertainty score0.786

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 head0.030
GPT teacher head0.228
Teacher spread0.198 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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