ArticleMedical & biological engineering & computing2025
Ultra-short-term stress measurement using RGB camera-based remote photoplethysmography with reduced effects of Individual differences in heart rate.
Article in Medical & biological engineering & computing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- When is rPPG physiologically valid for stress and workload monitoring? A PRISMA-ScR scoping review.Frontiers in digital health · 2026Pooled it
- Remote photoplethysmography for health assessment: a review informed by IntelliProve technology.Frontiers in digital health · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Stress is linked to health problems, increasing the need for immediate monitoring. Traditional methods like electrocardiograms or contact photoplethysmography require device attachment, causing discomfort, and ultra-short-term stress measurement research remains inadequate. This paper proposes a method for ultra-short-term stress monitoring using remote photoplethysmography (rPPG). Previous predictions of ultra-short-term stress have typically used pulse rate variability (PRV) features derived from time-segmented heart rate data. However, PRV varies at the same stress levels depending on heart rates, necessitating a new method to account for these differences. This study addressed this by segmenting rPPG data based on normal-to-normal intervals (NNIs), converted from peak-to-peak intervals, to predict ultra-short-term stress indices. We used NNI counts corresponding to average durations of 10, 20, and 30 s (13, 26, and 39 NNIs) to extract PRV features, predicting the Baevsky stress index through regressors. The Extra Trees Regressor achieved R
Indexed as
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.