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<p>Blood pressure is an essential indicator of cardiovascular health in the human body, and regular and accurate blood pressure measurement is essential for preventing cardiovascular diseases. The emergence of photoplethysmography (PPG) and the advancement of machine learning offers new opportunities for noninvasive blood pressure measurement. This paper proposes a non-contact method for measuring blood pressure using face video and machine learning. This method extracts facial remote photoplethysmography (RPPG) signals from face video captured by a camera, and enhances the signal quality of RPPG through a set of filtering processes. The blood pressure regression model is constructed using the extreme gradient boosting tree (XGBoost) method to estimate blood pressure from RPPG signals. This approach achieved accurate blood pressure measurement, with a measurement error of 4.8893 ± 6.6237 mmHg for systolic pressure and 4.0805 ± 5.5821 mmHg for diastolic pressure. Experimental results show that this method fully complies with the American Medical Instrumentation Association (AAMI).Our proposed method has minor errors in predicting the systolic and diastolic blood pressures and achieves grade A evaluation for both systolic and diastolic blood pressures according to the British Hypertension Society (BHS) standards.</p>

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A non-contact blood pressure measurement method based on face video

  • 期刊论文

  • 发表日期：

    2025年10月20日

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  • Lifeng Yang,
  • Shaojie Gu,
  • Binbin Liu,
  • Junjie Wang,
  • Junwei Cheng,
  • Yuanxi Zhang,
  • Zhengan Xia,
  • Yan Yang

摘要

Blood pressure is an essential indicator of cardiovascular health in the human body, and regular and accurate blood pressure measurement is essential for preventing cardiovascular diseases. The emergence of photoplethysmography (PPG) and the advancement of machine learning offers new opportunities for noninvasive blood pressure measurement. This paper proposes a non-contact method for measuring blood pressure using face video and machine learning. This method extracts facial remote photoplethysmography (RPPG) signals from face video captured by a camera, and enhances the signal quality of RPPG through a set of filtering processes. The blood pressure regression model is constructed using the extreme gradient boosting tree (XGBoost) method to estimate blood pressure from RPPG signals. This approach achieved accurate blood pressure measurement, with a measurement error of 4.8893 ± 6.6237 mmHg for systolic pressure and 4.0805 ± 5.5821 mmHg for diastolic pressure. Experimental results show that this method fully complies with the American Medical Instrumentation Association (AAMI).Our proposed method has minor errors in predicting the systolic and diastolic blood pressures and achieves grade A evaluation for both systolic and diastolic blood pressures according to the British Hypertension Society (BHS) standards.