{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T21:15:03Z","timestamp":1769721303093,"version":"3.49.0"},"reference-count":32,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T00:00:00Z","timestamp":1692835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Grant-in-Aid for Scientific Research (C) (Kakenhi)","award":["#17K01440"],"award-info":[{"award-number":["#17K01440"]}]},{"name":"Grant-in-Aid for Scientific Research (C) (Kakenhi)","award":["#21K12760"],"award-info":[{"award-number":["#21K12760"]}]},{"name":"Grant-in-Aid for Scientific Research (C) (Kakenhi)","award":["#23K02029"],"award-info":[{"award-number":["#23K02029"]}]},{"name":"Grant-in-Aid for Scientific Research (C) (Kakenhi)","award":["JP22dk0310111"],"award-info":[{"award-number":["JP22dk0310111"]}]},{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["#17K01440"],"award-info":[{"award-number":["#17K01440"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["#21K12760"],"award-info":[{"award-number":["#21K12760"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["#23K02029"],"award-info":[{"award-number":["#23K02029"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["JP22dk0310111"],"award-info":[{"award-number":["JP22dk0310111"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurately measuring blood pressure (BP) is essential for maintaining physiological health, which is commonly achieved using cuff-based sphygmomanometers. Several attempts have been made to develop cuffless sphygmomanometers. To increase their accuracy and long-term variability, machine learning methods can be applied for analyzing photoplethysmogram (PPG) signals. Here, we propose a method to estimate the BP during exercise using a cuffless device. The BP estimation process involved preprocessing signals, feature extraction, and machine learning techniques. To ensure the reliability of the signals extracted from the PPG, we employed the skewness signal quality index and the RReliefF algorithm for signal selection. Thereafter, the BP was estimated using the long short-term memory (LSTM)-based neural network. Seventeen young adult males participated in the experiments, undergoing a structured protocol composed of rest, exercise, and recovery for 20 min. Compared to the BP measured using a non-invasive voltage clamp-type continuous sphygmomanometer, that estimated by the proposed method exhibited a mean error of 0.32 \u00b1 7.76 mmHg, which is equivalent to the accuracy of a cuff-based sphygmomanometer per regulatory standards. By enhancing patient comfort and improving healthcare outcomes, the proposed approach can revolutionize BP monitoring in various settings, including clinical, home, and sports environments.<\/jats:p>","DOI":"10.3390\/s23177399","type":"journal-article","created":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T08:42:20Z","timestamp":1692952940000},"page":"7399","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Estimating Blood Pressure during Exercise with a Cuffless Sphygmomanometer"],"prefix":"10.3390","volume":"23","author":[{"given":"Kenta","family":"Hayashi","sequence":"first","affiliation":[{"name":"Institute of Systems and Information Engineering, University of Tsukuba, Tsukuba 305-8577, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuka","family":"Maeda","sequence":"additional","affiliation":[{"name":"Institute of Systems and Information Engineering, University of Tsukuba, Tsukuba 305-8577, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takumi","family":"Yoshimura","sequence":"additional","affiliation":[{"name":"Department of Medical and Welfare Engineering, Tokyo Metropolitan College of Industrial Technology, Tokyo 116-8523, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Data Science, Nagoya City University, Nagoya 467-8501, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8514-4200","authenticated-orcid":false,"given":"Toshiyo","family":"Tamura","sequence":"additional","affiliation":[{"name":"Future Robotics Organization, Waseda University, Tokyo 169-8050, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,24]]},"reference":[{"key":"ref_1","unstructured":"NCD Risk Factor Collaboration (NCD-RisC) (2021). 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