{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T03:03:32Z","timestamp":1792551812119,"version":"4.5.0"},"reference-count":42,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T00:00:00Z","timestamp":1733529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Science and Technology Council, Taiwan","award":["NSTC 111-2221-E-324-003-MY3"],"award-info":[{"award-number":["NSTC 111-2221-E-324-003-MY3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 \u00b1 9.71 mmHg and 1.52 \u00b1 6.32 mmHg, and in edge computing are 2.2 \u00b1 10.9 mmHg and 1.87 \u00b1 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.<\/jats:p>","DOI":"10.3390\/s24237830","type":"journal-article","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T11:16:48Z","timestamp":1733743008000},"page":"7830","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Using a Bodily Weight-Fat Scale for Cuffless Blood Pressure Measurement Based on the Edge Computing System"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3923-4387","authenticated-orcid":false,"given":"Shing-Hong","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9270-4191","authenticated-orcid":false,"given":"Bo-Yan","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4376-0806","authenticated-orcid":false,"given":"Xin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of AI Technology Development, M&D Data Science Center, Institute of Integrated Research, Institute of Science Tokyo, Tokyo 101-0062, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiun-Li","family":"Chin","sequence":"additional","affiliation":[{"name":"Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,7]]},"reference":[{"key":"ref_1","unstructured":"United Nations, Department of Economic Social Affairs (2019). 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