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Record W2747315159 · doi:10.1101/179812

Non-contact measurement of emotional and physiological changes in heart rate from a webcam

2017· preprint· en· W2747315159 on OpenAlexafffund
Christopher R. Madan, Tyler Harrison, Kyle E. Mathewson

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2017
Typepreprint
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsPhotoplethysmogramHeart rateStimulus (psychology)SittingSkin colorHeart rate variabilityBlood flowBlood pressureMedicineComputer visionPsychologyCardiologyComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

Abstract Heart rate, measured in beats per minute (BPM), can be used as an index of an individual’s physiological state. Each time the heart beats, blood is expelled and travels through the body. This blood flow can be detected in the face using a standard webcam that is able to pick up subtle changes in color that cannot be seen by the naked eye. Due to the light absorption spectrum of blood, we are able to detect differences in the amount of light absorbed by the blood traveling just below the skin (i.e., photoplethysmography). By modulating emotional and physiological stress—i.e., viewing arousing images and sitting vs. standing, respectively—to elicit changes in heart rate, we explored the feasibility of using a webcam as a psychophysiological measurement of autonomic activity. We found a high level of agreement between established physiological measures, electrocardiogram (ECG), and blood pulse oximetry, and heart rate estimates obtained from the webcam. We thus suggest webcams can be used as a non-invasive and readily available method for measuring psychophysiological changes, easily integrated into existing stimulus presentation software and hardware setups.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.032
GPT teacher head0.224
Teacher spread0.191 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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