ArticleNPJ digital medicine2025
The role of face regions in remote photoplethysmography for contactless heart rate monitoring.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The trial behind it
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Who cites it
9 citing papers in PubMed.
- EPI-Sense: an integrated hemodynamic sensing and analysis system for early pressure injury warning.Journal of translational medicine · 2026Article
- Lighting effects on optimal facial regions for remote heart rate measurement.NPJ cardiovascular health · 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
- Adaptive physiology-informed correction for reliable remote photoplethysmography heart-rate monitoring.NPJ digital medicine · 2026Article
- Balancing cardiac privacy with quality in video recordings.Communications medicine · 2025Article
- Demographic bias in public remote photoplethysmography datasets.NPJ digital medicine · 2025Article
- Remote photoplethysmography for health assessment: a review informed by IntelliProve technology.Frontiers in digital health · 2025Review
- State-dependent facial pulsation asymmetry and phase asynchrony measured by imaging photoplethysmography and their coupling with contingent negative variation in migraine.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heart rate (HR) estimation is crucial for early cardiovascular diagnosis, continuous monitoring, and various health applications. While electrocardiography (ECG) remains the gold standard, its discomfort and impracticality for continuous use have spurred the development of non-contact methods like remote photoplethysmography (rPPG). This systematic review (PROSPERO: CRD 42024592157) examines 70 studies to assess the impact of Region of Interest (ROI) selection on HR estimation accuracy. Most methods (36.8%) use the holistic face, while forehead and cheek areas (24.5% and 21.7%) show superior accuracy. Machine learning-based approaches outperform traditional methods under motion artifacts and poor lighting, achieving Mean Absolute Error and Root Mean Square Error below 1.0 for some datasets. Combining multiple patches improves performance, though increasing ROIs beyond 60 patches results in diminishing returns and higher computational complexity. These findings highlight the significance of ROI optimization for robust rPPG-based HR estimation.
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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.