{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T01:39:39Z","timestamp":1768786779005,"version":"3.49.0"},"reference-count":32,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,19]],"date-time":"2019-02-19T00:00:00Z","timestamp":1550534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["106-2221-E-002-213-MY3"],"award-info":[{"award-number":["106-2221-E-002-213-MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This work describes the development of a pressure-sensing array for noninvasive continuous blood pulse-wave monitoring. The sensing elements comprise a conductive polymer film and interdigital electrodes patterned on a flexible Parylene C substrate. The polymer film was patterned with microdome structures to enhance the acuteness of pressure sensing. The proposed device uses three pressure-sensing elements in a linear array, which greatly facilitates the blood pulse-wave measurement. The device exhibits high sensitivity (\u22120.533 kPa\u22121) and a fast dynamic response. Furthermore, various machine-learning algorithms, including random forest regression (RFR), gradient-boosting regression (GBR), and adaptive boosting regression (ABR), were employed for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) from the measured pulse-wave signals. Among these algorithms, the RFR-based method gave the best performance, with the coefficients of determination for the reference and estimated blood pressures being R2 = 0.871 for SBP and R2 = 0.794 for DBP, respectively.<\/jats:p>","DOI":"10.3390\/s19040848","type":"journal-article","created":{"date-parts":[[2019,2,20]],"date-time":"2019-02-20T03:05:52Z","timestamp":1550631952000},"page":"848","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["A Highly Sensitive Pressure-Sensing Array for Blood Pressure Estimation Assisted by Machine-Learning Techniques"],"prefix":"10.3390","volume":"19","author":[{"given":"Kuan-Hua","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, National Taiwan University, Taipei 10617, Taiwan"}]},{"given":"Fu","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Taiwan University, Taipei 10617, Taiwan"}]},{"given":"Tzung-Dau","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Medicine, National Taiwan University, Taipei 10617, Taiwan"},{"name":"National Taiwan University Hospital, Taipei 10002, Taiwan"}]},{"given":"Yao-Joe","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Taiwan University, Taipei 10617, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"World Health Organization (2013, April 03). 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