Using a Bodily Weight-Fat Scale for Cuffless Blood Pressure Measurement Based on the Edge Computing System
Blood pressure (BP) measurement is a major physiological information for people with cardiovascular diseases, such as hypertension, heart failure, and atherosclerosis. Moreover, elders and patients with kidney disease and diabetes mellitus also are suggested to measure their BP every day. The cuffle...
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MDPI AG
2024-12-01
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| author | Shing-Hong Liu Bo-Yan Wu Xin Zhu Chiun-Li Chin |
| author_facet | Shing-Hong Liu Bo-Yan Wu Xin Zhu Chiun-Li Chin |
| author_sort | Shing-Hong Liu |
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| description | Blood pressure (BP) measurement is a major physiological information for people with cardiovascular diseases, such as hypertension, heart failure, and atherosclerosis. Moreover, elders and patients with kidney disease and diabetes mellitus also are suggested to measure their BP every day. The cuffless BP measurement has been developed in the past 10 years, which is comfortable to users. Now, ballistocardiogram (BCG) and impedance plethysmogram (IPG) could be used to perform the cuffless BP measurement. Thus, the aim of this study is to realize edge computing for the BP measurement in real time, which includes measurements of BCG and IPG signals, digital signal process, feature extraction, and BP estimation by machine learning algorithm. This system measured BCG and IPG signals from a bodily weight-fat scale with the self-made circuits. The signals were filtered to reduce the noise and segmented by 2 s. Then, we proposed a flowchart to extract the parameter, pulse transit time (PTT), within each segment. The feature included two calibration-based parameters and one calibration-free parameter was used to estimate BP with XGBoost. In order to realize the system in STM32F756ZG NUCLEO development board, we limited the hyperparameters of XGBoost model, including maximum depth (max_depth) and tree number (n_estimators). Results show that the error of systolic blood pressure (SBP) and diastolic blood pressure (DBP) in server-based computing are 2.64 ± 9.71 mmHg and 1.52 ± 6.32 mmHg, and in edge computing are 2.2 ± 10.9 mmHg and 1.87 ± 6.79 mmHg. This proposed method significantly enhances the feasibility of bodily weight-fat scale in the BP measurement for effective utilization in mobile health applications. |
| format | Article |
| id | doaj-art-2fb239a6d8fe4277bb74dc2d3797ed41 |
| institution | OA Journals |
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| publishDate | 2024-12-01 |
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| spelling | doaj-art-2fb239a6d8fe4277bb74dc2d3797ed412025-08-20T02:38:42ZengMDPI AGSensors1424-82202024-12-012423783010.3390/s24237830Using a Bodily Weight-Fat Scale for Cuffless Blood Pressure Measurement Based on the Edge Computing SystemShing-Hong Liu0Bo-Yan Wu1Xin Zhu2Chiun-Li Chin3Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 41349, TaiwanDepartment of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 41349, TaiwanDepartment of AI Technology Development, M&D Data Science Center, Institute of Integrated Research, Institute of Science Tokyo, Tokyo 101-0062, JapanDepartment of Automatic Control Engineering, Feng Chia University, Taichung 40724, TaiwanBlood pressure (BP) measurement is a major physiological information for people with cardiovascular diseases, such as hypertension, heart failure, and atherosclerosis. Moreover, elders and patients with kidney disease and diabetes mellitus also are suggested to measure their BP every day. The cuffless BP measurement has been developed in the past 10 years, which is comfortable to users. Now, ballistocardiogram (BCG) and impedance plethysmogram (IPG) could be used to perform the cuffless BP measurement. Thus, the aim of this study is to realize edge computing for the BP measurement in real time, which includes measurements of BCG and IPG signals, digital signal process, feature extraction, and BP estimation by machine learning algorithm. This system measured BCG and IPG signals from a bodily weight-fat scale with the self-made circuits. The signals were filtered to reduce the noise and segmented by 2 s. Then, we proposed a flowchart to extract the parameter, pulse transit time (PTT), within each segment. The feature included two calibration-based parameters and one calibration-free parameter was used to estimate BP with XGBoost. In order to realize the system in STM32F756ZG NUCLEO development board, we limited the hyperparameters of XGBoost model, including maximum depth (max_depth) and tree number (n_estimators). Results show that the error of systolic blood pressure (SBP) and diastolic blood pressure (DBP) in server-based computing are 2.64 ± 9.71 mmHg and 1.52 ± 6.32 mmHg, and in edge computing are 2.2 ± 10.9 mmHg and 1.87 ± 6.79 mmHg. This proposed method significantly enhances the feasibility of bodily weight-fat scale in the BP measurement for effective utilization in mobile health applications.https://www.mdpi.com/1424-8220/24/23/7830blood pressure measurementballistocardiogramimpedance plethysmogrambodily weight-fat scaleedge computing |
| spellingShingle | Shing-Hong Liu Bo-Yan Wu Xin Zhu Chiun-Li Chin Using a Bodily Weight-Fat Scale for Cuffless Blood Pressure Measurement Based on the Edge Computing System Sensors blood pressure measurement ballistocardiogram impedance plethysmogram bodily weight-fat scale edge computing |
| title | Using a Bodily Weight-Fat Scale for Cuffless Blood Pressure Measurement Based on the Edge Computing System |
| title_full | Using a Bodily Weight-Fat Scale for Cuffless Blood Pressure Measurement Based on the Edge Computing System |
| title_fullStr | Using a Bodily Weight-Fat Scale for Cuffless Blood Pressure Measurement Based on the Edge Computing System |
| title_full_unstemmed | Using a Bodily Weight-Fat Scale for Cuffless Blood Pressure Measurement Based on the Edge Computing System |
| title_short | Using a Bodily Weight-Fat Scale for Cuffless Blood Pressure Measurement Based on the Edge Computing System |
| title_sort | using a bodily weight fat scale for cuffless blood pressure measurement based on the edge computing system |
| topic | blood pressure measurement ballistocardiogram impedance plethysmogram bodily weight-fat scale edge computing |
| url | https://www.mdpi.com/1424-8220/24/23/7830 |
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