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Non-contact Heart Rate and Respiratory Rate Estimation from Videos of the Neck

2024· article· en· W4405488835 on OpenAlexaff
Tianyu Zhang, Miodrag Bolić, Mohammad Hossein Davood Abadi Farahani, Terri Zadorsky

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsOntario Clinical Oncology GroupMontfort HospitalUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceRespiratory systemEstimationHeart rateCardiologyMedicineInternal medicineEngineering

Abstract

fetched live from OpenAlex

Heart Rate (HR) and Respiratory Rate (RR) estimation constitutes a crucial part of non-contact assessment of cardiovascular disease, which has been a leading cause of death worldwide. This paper proposes a novel HR and RR estimation algorithm based on RGB videos recorded by a smartphone camera. Instead of requiring facial videos, this novel algorithm demonstrates the ability to estimate HR and RR using only a video of the human neck. This novel algorithm captures cardiac as well as respiratory activity via detecting skin displacement by only analyzing the Laplacian pyramid of each frame of the video. Its performance was evaluated by applying it to the videos of neck of 80 participants and comparing it to existing methods, demonstrating the superior performance of the proposed algorithm.

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 score0.097
Threshold uncertainty score0.276

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 head0.011
GPT teacher head0.226
Teacher spread0.215 · 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
Published2024
Admission routes1
Has abstractyes

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