Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation
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 w...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/4/1753 |
| _version_ | 1797618366738857984 |
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| author | Batol Hamoud Alexey Kashevnik Walaa Othman Nikolay Shilov |
| author_facet | Batol Hamoud Alexey Kashevnik Walaa Othman Nikolay Shilov |
| author_sort | Batol Hamoud |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T08:11:59Z |
| format | Article |
| id | doaj.art-99ab8aa281eb4c858c4896f00293182f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T08:11:59Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-99ab8aa281eb4c858c4896f00293182f2023-11-16T23:05:38ZengMDPI AGSensors1424-82202023-02-01234175310.3390/s23041753Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and EvaluationBatol Hamoud0Alexey Kashevnik1Walaa Othman2Nikolay Shilov3Information Technology and Programming Faculty, ITMO University, St. Petersburg 197101, RussiaSt. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg 199178, RussiaInformation Technology and Programming Faculty, ITMO University, St. Petersburg 197101, RussiaSt. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg 199178, RussiaOne 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.https://www.mdpi.com/1424-8220/23/4/1753blood pressure estimationneural networkcomputer visionphotoplethysmography |
| spellingShingle | Batol Hamoud Alexey Kashevnik Walaa Othman Nikolay Shilov Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation Sensors blood pressure estimation neural network computer vision photoplethysmography |
| title | Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation |
| title_full | Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation |
| title_fullStr | Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation |
| title_full_unstemmed | Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation |
| title_short | Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation |
| title_sort | neural network model combination for video based blood pressure estimation new approach and evaluation |
| topic | blood pressure estimation neural network computer vision photoplethysmography |
| url | https://www.mdpi.com/1424-8220/23/4/1753 |
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