Contactless blood pressure sensing using facial visible and thermal images

Oiwa, Kosuke; Bando, Shizuka; Nozawa, Akio

doi:10.1007/s10015-018-0450-1

Cited by 16 publications

(8 citation statements)

References 17 publications

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“…Other approaches such as that of Secerbegovic et al [ 3 ] used the pulse transit time to estimate blood pressure after applying independent component analysis (ICA) [ 9 ] on the raw source signals extracted from the forehead; their linear regression model achieved a total mean average error (MAE) of 9.48 mmHg for SBP and 4.48 mmHg for mean arterial pressure. ICA and linear regression were also used in [ 10 ], where Oiwa et al tried to increase the accuracy of the estimated blood pressure by using ICA for processing the RGB signals of five obtained ROIs and by using facial PPG amplitude and nasal skin temperature as inputs of linear regression models, which predicted blood pressure with an MAE within the range of 1.5–4.5 mmHg and 1.72–4.75 mmHg, respectively.…”

Section: Related Workmentioning

confidence: 99%

Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation

Hamoud

Kashevnik

Othman

et al. 2023

Sensors

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One of the most effective vital signs of health conditions is blood pressure. It has such an impact that changes your state from completely relaxed to extremely unpleasant, which makes the task of blood pressure monitoring a main procedure that almost everyone undergoes whenever there is something wrong or suspicious with his/her health condition. The most popular and accurate ways to measure blood pressure are cuff-based, inconvenient, and pricey, but on the bright side, many experimental studies prove that changes in the color intensities of the RGB channels represent variation in the blood that flows beneath the skin, which is strongly related to blood pressure; hence, we present a novel approach to blood pressure estimation based on the analysis of human face video using hybrid deep learning models. We deeply analyzed proposed approaches and methods to develop combinations of state-of-the-art models that were validated by their testing results on the Vision for Vitals (V4V) dataset compared to the performance of other available proposed models. Additionally, we came up with a new metric to evaluate the performance of our models using Pearson’s correlation coefficient between the predicted blood pressure of the subjects and their respiratory rate at each minute, which is provided by our own dataset that includes 60 videos of operators working on personal computers for almost 20 min in each video. Our method provides a cuff-less, fast, and comfortable way to estimate blood pressure with no need for any equipment except the camera of your smartphone.

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Section: Related Workmentioning

confidence: 99%

Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation

Hamoud

Kashevnik

Othman

et al. 2023

Sensors

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“…It obtained AEs of

mmHg for SBP and

mmHg for DBP. The authors of [ 223 ] also applied ICA in the preprocessing method. Instead of using the PTT, they introduced a regression model which uses the rPPG waveform feature indices and facial skin temperature as inputs for estimating BP (see Figure 15 for reference as well).…”

Section: Contactless Bp Measurement From Rppg Signalsmentioning

confidence: 99%

“… A pipeline of traditional machine learning (TML) methods for rPPG signals adopted in [ 220 , 223 ]. Those rPPG training data are initially preprocessed.…”

Section: Figurementioning

confidence: 99%

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

et al. 2022

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Blood pressure (BP) determines whether a person has hypertension and offers implications as to whether he or she could be affected by cardiovascular disease. Cuff-based sphygmomanometers have traditionally provided both accuracy and reliability, but they require bulky equipment and relevant skills to obtain precise measurements. BP measurement from photoplethysmography (PPG) signals has become a promising alternative for convenient and unobtrusive BP monitoring. Moreover, the recent developments in remote photoplethysmography (rPPG) algorithms have enabled new innovations for contactless BP measurement. This paper illustrates the evolution of BP measurement techniques from the biophysical theory, through the development of contact-based BP measurement from PPG signals, and to the modern innovations of contactless BP measurement from rPPG signals. We consolidate knowledge from a diverse background of academic research to highlight the importance of multi-feature analysis for improving measurement accuracy. We conclude with the ongoing challenges, opportunities, and possible future directions in this emerging field of research.

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“…Subjects were permitted small head movements to simulate realistic work conditions. Oiwa et al ( 56 ) correlated facial PPG amplitude with reference BP following ICA, obtaining a mean absolute error in the range 1.50–4.15 over eight subjects. Data was acquired over a series of 2-min resting state segments with eyes closed and 1-min cold stimulus state segment, where subjects placed their hand in a cold (14°C) water bath with eyes opened.…”

Section: Non-contact Video Camera Measurement Of Blood Pressurementioning

confidence: 99%

Smartphones and Video Cameras: Future Methods for Blood Pressure Measurement

Steinman

Barszczyk

Sun

et al. 2021

Front. Digit. Health

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Regular blood pressure (BP) monitoring enables earlier detection of hypertension and reduces cardiovascular disease. Cuff-based BP measurements require equipment that is inconvenient for some individuals and deters regular home-based monitoring. Since smartphones contain sensors such as video cameras that detect arterial pulsations, they could also be used to assess cardiovascular health. Researchers have developed a variety of image processing and machine learning techniques for predicting BP via smartphone or video camera. This review highlights research behind smartphone and video camera methods for measuring BP. These methods may in future be used at home or in clinics, but must be tested over a larger range of BP and lighting conditions. The review concludes with a discussion of the advantages of the various techniques, their potential clinical applications, and future directions and challenges. Video cameras may potentially measure multiple cardiovascular metrics including and beyond BP, reducing the risk of cardiovascular disease.

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Contactless blood pressure sensing using facial visible and thermal images

Cited by 16 publications

References 17 publications

Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation

Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

Smartphones and Video Cameras: Future Methods for Blood Pressure Measurement

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