Key result
Contactless blood pressure estimation using facial visible image analysis and individual regression models achieved a minimum mean absolute error of approximately 2 mmHg compared to continuous hemodynamometry.
Why the study?
Can contactless blood pressure assessment using facial visible image analysis accurately estimate blood pressure?
Observational (n=8)
No
Can contactless blood pressure assessment using facial visible image analysis accurately estimate blood pressure?
Contactless facial PPG BP estimation proposed without validation; leaves open accuracy for ubiquitous monitoring and requires prospective testing.
Hypertension is one of the leading risk factors for cerebrovascular, cardiovascular, and chronic kidney diseases. Regular measurement and monitoring of blood pressure is important to decrease or prevent pathogenesis of diseases. Contactless measurement of blood pressure using a smartphone application, can enable regular monitoring. The objective of this study is to construct a system which can monitor blood pressure anytime and anywhere. In this study, contactless blood pressure assessment was attempted, using facial visible image analysis and created individual models for blood pressure estimation. Variation in brightness of the skin color was obtained from facial visible images and applied to independent component analysis, which is one of the blind source separation methods to extract the facial photoplethysmogram (PPG) component using a proposed system. Amplitude and phase of facial PPG component were used as indices for blood pressure. A correlation analysis between facial PPG component and blood pressure was performed and created individual models for blood pressure estimation.
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Oiwa et al. (2018) conducted an observational in Healthy (n=8). Facial visible image analysis (facial PPG) vs. Continuous hemodynamometer was evaluated on Mean absolute error between measured and estimated blood pressure. Contactless blood pressure estimation using facial visible image analysis and individual regression models achieved a minimum mean absolute error of approximately 2 mmHg compared to continuous hemodynamometry.
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