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Contactless Blood Pressure Estimation System Using a Computer Vision System
Blood pressure (BP) is one of the most common vital signs related to cardiovascular diseases. BP is traditionally measured by mercury, aneroid, or digital sphygmomanometers; however, these approaches are restrictive, inconvenient, and need a pressure cuff to be attached directly to the patient. Ther...
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Published in: | Inventions (Basel) 2022-09, Vol.7 (3), p.84 |
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description | Blood pressure (BP) is one of the most common vital signs related to cardiovascular diseases. BP is traditionally measured by mercury, aneroid, or digital sphygmomanometers; however, these approaches are restrictive, inconvenient, and need a pressure cuff to be attached directly to the patient. Therefore, it is clinically important to develop an innovative system that can accurately measure BP without the need for any direct physical contact with the people. This work aims to create a new computer vision system that remotely measures BP using a digital camera without a pressure cuff. The proposed BP system extracts the optical properties of photoplethysmographic signals in two regions in the forehead captured by a digital camera and calculates BP based on specific formulas. The experiments were performed on 25 human participants with different skin tones and repeated at different times under ambient light conditions. Compared to the systolic/diastolic BP readings obtained from a commercial digital sphygmomanometer, the proposed BP system achieves an accuracy of 94.6% with a root mean square error (RMSE) of 9.2 mmHg for systolic BP readings and an accuracy of 95.4% with an RMSE of 7.6 mmHg for diastolic BP readings. Thus, the proposed BP system has the potential of being a promising tool in the upcoming generation of BP monitoring systems. |
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BP is traditionally measured by mercury, aneroid, or digital sphygmomanometers; however, these approaches are restrictive, inconvenient, and need a pressure cuff to be attached directly to the patient. Therefore, it is clinically important to develop an innovative system that can accurately measure BP without the need for any direct physical contact with the people. This work aims to create a new computer vision system that remotely measures BP using a digital camera without a pressure cuff. The proposed BP system extracts the optical properties of photoplethysmographic signals in two regions in the forehead captured by a digital camera and calculates BP based on specific formulas. The experiments were performed on 25 human participants with different skin tones and repeated at different times under ambient light conditions. Compared to the systolic/diastolic BP readings obtained from a commercial digital sphygmomanometer, the proposed BP system achieves an accuracy of 94.6% with a root mean square error (RMSE) of 9.2 mmHg for systolic BP readings and an accuracy of 95.4% with an RMSE of 7.6 mmHg for diastolic BP readings. Thus, the proposed BP system has the potential of being a promising tool in the upcoming generation of BP monitoring systems.</description><identifier>ISSN: 2411-5134</identifier><identifier>EISSN: 2411-5134</identifier><identifier>DOI: 10.3390/inventions7030084</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Blood pressure ; Cardiovascular diseases ; Computer vision ; contactless BP monitoring ; digital camera ; Digital cameras ; Electrocardiography ; Electronic cameras ; face detection ; Forehead ; Human performance ; Machine vision ; Medical instruments ; Neural networks ; Optical communication ; Optical properties ; Physiology ; Root-mean-square errors ; Sensors ; Time series ; video plethysmography ; Vision systems ; Wavelet transforms</subject><ispartof>Inventions (Basel), 2022-09, Vol.7 (3), p.84</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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BP is traditionally measured by mercury, aneroid, or digital sphygmomanometers; however, these approaches are restrictive, inconvenient, and need a pressure cuff to be attached directly to the patient. Therefore, it is clinically important to develop an innovative system that can accurately measure BP without the need for any direct physical contact with the people. This work aims to create a new computer vision system that remotely measures BP using a digital camera without a pressure cuff. The proposed BP system extracts the optical properties of photoplethysmographic signals in two regions in the forehead captured by a digital camera and calculates BP based on specific formulas. The experiments were performed on 25 human participants with different skin tones and repeated at different times under ambient light conditions. Compared to the systolic/diastolic BP readings obtained from a commercial digital sphygmomanometer, the proposed BP system achieves an accuracy of 94.6% with a root mean square error (RMSE) of 9.2 mmHg for systolic BP readings and an accuracy of 95.4% with an RMSE of 7.6 mmHg for diastolic BP readings. Thus, the proposed BP system has the potential of being a promising tool in the upcoming generation of BP monitoring systems.</description><subject>Blood pressure</subject><subject>Cardiovascular diseases</subject><subject>Computer vision</subject><subject>contactless BP monitoring</subject><subject>digital camera</subject><subject>Digital cameras</subject><subject>Electrocardiography</subject><subject>Electronic cameras</subject><subject>face detection</subject><subject>Forehead</subject><subject>Human performance</subject><subject>Machine vision</subject><subject>Medical instruments</subject><subject>Neural networks</subject><subject>Optical communication</subject><subject>Optical properties</subject><subject>Physiology</subject><subject>Root-mean-square errors</subject><subject>Sensors</subject><subject>Time series</subject><subject>video plethysmography</subject><subject>Vision systems</subject><subject>Wavelet transforms</subject><issn>2411-5134</issn><issn>2411-5134</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>COVID</sourceid><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptkVFrGzEMx03pYCXrB9jbQZ_T2pbvfH5sQ9aWFjpYtlej-HTBIXdObWeQb1-3GctGi0GWZOnHXxZjXwW_BDD8yo-_acw-jElz4LxVJ-xMKiGmtQB1-o__mZ2ntOaci7aG2pgz9jALY0aXN5RSdbMJoau-x-LvIlXzlP2Ar9zqxz5lGqqfyY-rCqtZGLa7TLH65dPx-Qv71OMm0fmfe8IW3-aL2d308en2fnb9OHVKmTwVS-OWghR3DmtyXSOL6ZdAgpcQUTrR1K1uW02dbBpQoPrOaeix04okTNj9AdsFXNttLBrj3gb09i0R4spizN5tyJICB6ZBhcaoWpgWapIodA9toyWqwro4sLYxPO8oZbsOuzgW9VbqIkNJEHCsWmGB-rEPOaIbfHL2WivVaN6U352wyw-qyulo8C6M1PuS_69BHBpcDClF6v8OI7h9Xax9t1h4Af5Sl1E</recordid><startdate>20220901</startdate><enddate>20220901</enddate><creator>Al-Naji, Ali</creator><creator>Fakhri, Ahmed Bashar</creator><creator>Mahmood, Mustafa F</creator><creator>Chahl, Javaan</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-8840-9235</orcidid><orcidid>https://orcid.org/0000-0001-6061-802X</orcidid><orcidid>https://orcid.org/0000-0001-6496-0543</orcidid></search><sort><creationdate>20220901</creationdate><title>Contactless Blood Pressure Estimation System Using a Computer Vision System</title><author>Al-Naji, Ali ; 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subjects | Blood pressure Cardiovascular diseases Computer vision contactless BP monitoring digital camera Digital cameras Electrocardiography Electronic cameras face detection Forehead Human performance Machine vision Medical instruments Neural networks Optical communication Optical properties Physiology Root-mean-square errors Sensors Time series video plethysmography Vision systems Wavelet transforms |
title | Contactless Blood Pressure Estimation System Using a Computer Vision System |
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