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<p>Remote photoplethysmography (rPPG) is a non-contact singal extracting photoplethysmographic (PPG) waveforms from video by analyzing blood-volume-induced modulations in the light reflected from the skin. As rPPG moves closer to real-world deployment, comprehensive datasets are essential for the development of robust algorithms across diverse conditions and populations. Building on existing efforts, we introduce the MPU-rPPG dataset, designed to bridge current gaps and establish a new gold standard for rPPG research. This dataset captures high-fidelity physiological signals from multiple body regions (including the face and limbs) under varying light conditions and environments. It supports heart rate ranges from 50–160 bpm, accommodating both resting states and high-intensity activities, and includes a diverse set of subjects across different skin tones and demographics. The versatility of MPU-rPPG supports robustness studies relevant to practical scenarios—including clinical monitoring environments and highly dynamic public activities (e.g., endurance sports or driving)—where motion, illumination variation, and partial occlusion remain major challenges for reliable rPPG. By addressing these critical needs, the MPU-rPPG dataset provides a comprehensive foundation for the development of real-world rPPG systems and is committed to setting a new benchmark for future research.</p>

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MPU-rPPG: A Comprehensive and High-Fidelity Dataset for Remote Photoplethysmography Across Diverse Conditions and Demographics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月28日

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  • Zhengxuan Chen,
  • Tao Tan,
  • Zitong Yu,
  • Haitao Jiang,
  • Keguang Wang,
  • Jie Gao,
  • Yue Sun

摘要

Remote photoplethysmography (rPPG) is a non-contact singal extracting photoplethysmographic (PPG) waveforms from video by analyzing blood-volume-induced modulations in the light reflected from the skin. As rPPG moves closer to real-world deployment, comprehensive datasets are essential for the development of robust algorithms across diverse conditions and populations. Building on existing efforts, we introduce the MPU-rPPG dataset, designed to bridge current gaps and establish a new gold standard for rPPG research. This dataset captures high-fidelity physiological signals from multiple body regions (including the face and limbs) under varying light conditions and environments. It supports heart rate ranges from 50–160 bpm, accommodating both resting states and high-intensity activities, and includes a diverse set of subjects across different skin tones and demographics. The versatility of MPU-rPPG supports robustness studies relevant to practical scenarios—including clinical monitoring environments and highly dynamic public activities (e.g., endurance sports or driving)—where motion, illumination variation, and partial occlusion remain major challenges for reliable rPPG. By addressing these critical needs, the MPU-rPPG dataset provides a comprehensive foundation for the development of real-world rPPG systems and is committed to setting a new benchmark for future research.