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Record W2334598136 · doi:10.15353/vsnl.v1i1.43

Remote Heart Rate Measurement through Broadband Video via Stochastic Bayesian Estimation

2015· article· en· W2334598136 on OpenAlex
Brendan Chwyl, Audrey G. Chung, Jason Deglint, Alexander Wong, David A. Clausi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueVision Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsUniversity of Waterloo
FundersMinistero dello Sviluppo EconomicoNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsOntario Ministry of Economic Development and Innovation
KeywordsBayesian probabilityBroadbandMonte Carlo methodComputer scienceMathematicsAlgorithmStatisticsTelecommunications

Abstract

fetched live from OpenAlex

<p>A novel method for remote heart rate sensing via standard broadband<br />video is proposed. Points are stochastically sampled from the<br />cheek region and tracked throughout the video, producing a set<br />of skin erythema time series. From these observations, a photoplethysmogram<br />(PPG) is estimated via Bayesian minimization, with<br />the required posterior probability estimated through an importanceweighted<br />Monte Carlo approach. From the estimated PPG, an estimated<br />heart rate is produced through frequency domain analysis.<br />Results indicate improved accuracy over current state of the art<br />methods.</p>

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.931

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 head0.026
GPT teacher head0.253
Teacher spread0.226 · 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