The role of face regions in remote photoplethysmography for contactless heart rate monitoring.

Journal: NPJ digital medicine
Published Date: Jul 26, 2025

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.

Authors

  • Maksym Bondarenko
    Biomedical and Mobile Health Technology Research Lab, ETH Zürich, 8008, Zürich, Switzerland.
  • Carlo Menon
    Menrva Research Group, Schools of Mechatronic Systems & Engineering Science at Simon Fraser University (SFU), Burnaby, BC V5A 1S6, Canada. cmenon@sfu.ca.
  • Mohamed Elgendi
    School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.

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