Estimation of resting blood pressure using facial thermal images by separating acute stress variations

The increasing number of people with hypertension worldwide has become a matter of grave concern. Blood pressure monitoring using a non-contact measurement technique is expected to detect and control this medical condition. Previous studies have estimated blood pressure variations following an acute...

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Bibliographic Details
Published in:Artificial life and robotics 2021-11, Vol.26 (4), p.473-480
Main Authors: Iwashita, Yuki, Nagumo, Kent, Oiwa, Kosuke, Nozawa, Akio
Format: Article
Language:English
Subjects:
Artificial Intelligence
Blood pressure
Circadian rhythms
Computation by Abstract Devices
Computer Science
Contact pressure
Control
Hypertension
Independent component analysis
Infrared imagery
Infrared imaging
Measurement techniques
Mechatronics
Multiple regression analysis
Original Article
Robotics
Thermography
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Summary:The increasing number of people with hypertension worldwide has become a matter of grave concern. Blood pressure monitoring using a non-contact measurement technique is expected to detect and control this medical condition. Previous studies have estimated blood pressure variations following an acute stress response based on facial thermal images obtained from infrared thermography devices. However, a non-contact resting blood pressure estimation method is required because blood pressure is generally measured in the resting state without inducing acute stress. Day-long blood pressure variations include short-term variations due to acute stress and long-term variations in circadian rhythms. The aim of this study is to estimate resting blood pressure from facial thermal images by separating and excluding short-term variations related to acute stress. To achieve this, short-term blood pressure variations components related to acute stress on facial thermal images were separated using independent component analysis. Resting blood pressure was estimated with the extracted independent components excluding the short-term components using multiple regression analysis. The results show that the proposed approach can accurately estimate resting blood pressure from facial thermal images, with a 9.90 mmHg root mean square error. In addition, features related to resting blood pressure were represented in the nose, lip, and cheek regions.
ISSN:1433-5298
1614-7456
DOI:10.1007/s10015-021-00705-y