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<p>Remote photoplethysmography (rPPG) enables non-contact heart rate (HR) and waveform measurement from facial video, offering advantages over contact methods while contending with motion, illumination changes, compression, and frame-rate variability. This roadmap integrates a systematic review, technical synthesis, and a <i>clinic-acquired</i> demonstration to accelerate rPPG research and translation. We outline the measurement pipeline and synthesize progress across eight domains: datasets, device factors, data scarcity, skin/ROI detection, model- and data-driven algorithms, filtering, and evaluation metrics. Strategies for improved robustness include targeted dataset expansion, augmentation, optimized ROI policies, motion and photometric normalization, and hardware configuration. A proof-of-feasibility using data collected in a routine clinical setting alongside reference monitors demonstrates high signal-level agreement under a multi-metric framework (correlation/concordance, absolute errors, and Bland–Altman bias/dispersion). We conclude with recommendations for accuracy, efficiency, and deployment in real-world and clinical contexts, providing a consolidated resource for researchers and clinicians and a pragmatic path toward reliable clinical adoption.</p>

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Roadmap of remote photoplethysmography from heart rate measurement toward clinical translation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月9日

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  • Mohamed Elgendi,
  • Shuo Li,
  • Carlo Menon,
  • Edmund Y. Lam,
  • Basem Al-Omari,
  • Stefan Borik,
  • Kirk H. Shelley,
  • Aymen Alian

摘要

Remote photoplethysmography (rPPG) enables non-contact heart rate (HR) and waveform measurement from facial video, offering advantages over contact methods while contending with motion, illumination changes, compression, and frame-rate variability. This roadmap integrates a systematic review, technical synthesis, and a clinic-acquired demonstration to accelerate rPPG research and translation. We outline the measurement pipeline and synthesize progress across eight domains: datasets, device factors, data scarcity, skin/ROI detection, model- and data-driven algorithms, filtering, and evaluation metrics. Strategies for improved robustness include targeted dataset expansion, augmentation, optimized ROI policies, motion and photometric normalization, and hardware configuration. A proof-of-feasibility using data collected in a routine clinical setting alongside reference monitors demonstrates high signal-level agreement under a multi-metric framework (correlation/concordance, absolute errors, and Bland–Altman bias/dispersion). We conclude with recommendations for accuracy, efficiency, and deployment in real-world and clinical contexts, providing a consolidated resource for researchers and clinicians and a pragmatic path toward reliable clinical adoption.