ArticleNPJ digital medicine2021
Evaluation of biases in remote photoplethysmography methods.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
What it found
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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
31 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
- Artificial Intelligence Enabled Diagnostics Using Photoplethysmography (PPG): Beyond SpOLife (Basel, Switzerland) · 2026Review
- Missed isochromatic cardiac pulsation in remote photoplethysmography: detection, impact, and removal.Biomedical optics express · 2026Article
- Roadmap of remote photoplethysmography from heart rate measurement toward clinical translation.NPJ digital medicine · 2026Article
- Advancing remote photoplethysmography (rPPG) to facilitate cardiac monitoring in naturalistic settings using webcam technology.Behavior research methods · 2026Article
- DeepRespNet: a hybrid attention-recurrent framework for non-contact respiratory rate estimation.Frontiers in physiology · 2026Article
- Feasibility of heart rate monitoring in hemodialysis patients using visible imaging: a pilot study.Frontiers in physiology · 2026Article
- Non invasive blood glucose estimation using green light photoplethysmography and machine learning.Frontiers in digital health · 2026Article
- Barriers to translating continuous monitoring technologies for preventative medicine.Nature biomedical engineering · 2025Review
- Demographic bias in public remote photoplethysmography datasets.NPJ digital medicine · 2025Article
- Remote Vital Sensing in Clinical Veterinary Medicine: A Comprehensive Review of Recent Advances, Accomplishments, Challenges, and Future Perspectives.Animals : an open access journal from MDPI · 2025Review
- Validation of a Multi-Channel Ambient Sensor to Measure Vital Signs in Patients Within the Ward and at Home.Sensors (Basel, Switzerland) · 2025Observational
- Remote photoplethysmography for health assessment: a review informed by IntelliProve technology.Frontiers in digital health · 2025Review
- Video-based estimation of blood pressure.PloS one · 2025Article
- A machine learning-based approach for constructing remote photoplethysmogram signals from video cameras.Communications medicine · 2024Article
- Contactless Blood Oxygen Saturation Estimation from Facial Videos Using Deep Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Remote physiological signal recovery with efficient spatio-temporal modeling.Frontiers in physiology · 2024Article
- The Evaluation of Remote Monitoring Technology Across Participants With Different Skin Tones.Cureus · 2023Article
- Evaluating the Potential of an Oral-Based Bioguard to Estimate Heart Rate Using Photoplethysmography.Biosensors · 2023Article
- Contactless Camera-Based Heart Rate and Respiratory Rate Monitoring Using AI on Hardware.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This work investigates the estimation biases of remote photoplethysmography (rPPG) methods for pulse rate measurement across diverse demographics. Advances in photoplethysmography (PPG) and rPPG methods have enabled the development of contact and noncontact approaches for continuous monitoring and collection of patient health data. The contagious nature of viruses such as COVID-19 warrants noncontact methods for physiological signal estimation. However, these approaches are subject to estimation biases due to variations in environmental conditions and subject demographics. The performance of contact-based wearable sensors has been evaluated, using off-the-shelf devices across demographics. However, the measurement uncertainty of rPPG methods that estimate pulse rate has not been sufficiently tested across diverse demographic populations or environments. Quantifying the efficacy of rPPG methods in real-world conditions is critical in determining their potential viability as health monitoring solutions. Currently, publicly available face datasets accompanied by physiological measurements are typically captured in controlled laboratory settings, lacking diversity in subject skin tones, age, and cultural artifacts (e.g, bindi worn by Indian women). In this study, we collect pulse rate and facial video data from human subjects in India and Sierra Leone, in order to quantify the uncertainty in noncontact pulse rate estimation methods. The video data are used to estimate pulse rate using state-of-the-art rPPG camera-based methods, and compared against ground truth measurements captured using an FDA-approved contact-based pulse rate measurement device. Our study reveals that rPPG methods exhibit similar biases when compared with a contact-based device across demographic groups and environmental conditions. The mean difference between pulse rates measured by rPPG methods and the ground truth is found to be ~2% (1 beats per minute (b.p.m.)), signifying agreement of rPPG methods with the ground truth. We also find that rPPG methods show pulse rate variability of ~15% (11 b.p.m.), as compared to the ground truth. We investigate factors impacting rPPG methods and discuss solutions aimed at mitigating variance.
Identifiers
What OpenQuestion holds
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.