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Record W4399760043 · doi:10.32920/26052412

Remote Physiological Signal Acquisition and Analysis: Contactless Photoplethysmography and Specular Reflection Vascular Imaging

2024· preprint· en· W4399760043 on OpenAlexaff
Timothy Burton

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsToronto Metropolitan UniversityQueen's University
Fundersnot available
KeywordsPhotoplethysmogramSpecular reflectionSIGNAL (programming language)Reflection (computer programming)Remote sensingSpecular highlightComputer scienceComputer visionOpticsGeographyPhysics

Abstract

fetched live from OpenAlex

In this thesis, I describe the remote acquisition of physiological signals using contactless remote photoplethysmography (rPPG), and the novel specular reflection vascular imaging (SRVI). rPPG is a contactless extension of reflection photoplethysmography, which captures the variations in skin optical properties due to changes in the primary light absorber in blood - hemoglobin. I used rPPG (embodied in a consumer smartphone) to investigate various physiological phenomena, including ischemia, Mayer waves, venous outflow, and characteristics of glabrous vs. non-glabrous skin. Despite its value, photoplethysmography is limited by sensitivity to melanin concentration, and therefore has performance limitations in acquisition from subjects with darker skin tones. To address this limitation, SRVI was proposed to leverage specular reflection for acquisition of skin displacements caused by mechanical pulsations in blood vessels. SRVI is insensitive to melanin concentration since acquisition is restricted to the very surface of the skin. SRVI was capable of capturing carotid artery and jugular vein waveforms.

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 score0.587
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.242
Teacher spread0.230 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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