{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:00:42Z","timestamp":1779382842806,"version":"3.53.1"},"reference-count":40,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pulse wave and pulse rate are important indicators of cardiovascular health. Technologies that can check the pulse by contacting the skin with optical sensors built into smart devices have been developed. However, this may cause inconvenience, such as foreign body sensation. Accordingly, studies have been conducted on non-contact pulse rate measurements using facial videos focused on the indoors. Moreover, since the majority of studies are conducted indoors, the error in the pulse rate measurement in outdoor environments, such as an outdoor bench, car and drone, is high. In this paper, to deal with this issue, we focus on developing a robust pulse measurement method based on facial videos taken in diverse environments. The proposed method stably detects faces by removing high-frequency components of face coordinate signals derived from fine body tremors and illumination conditions. It optimizes for extracting skin color changes by reducing illumination-caused noise using the Cg color difference component. The robust pulse wave is extracted from the Cg signal using FFT\u2013iFFT with zero-padding. It can eliminate signal-filtering distortion effectively. We demonstrate that the proposed method relieves pulse rate measurement problems, producing 3.36, 5.81, and 6.09 bpm RMSE for an outdoor bench, driving car, and flying drone, respectively.<\/jats:p>","DOI":"10.3390\/s22239373","type":"journal-article","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T03:28:04Z","timestamp":1669951684000},"page":"9373","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Robust Pulse Rate Measurements from Facial Videos in Diverse Environments"],"prefix":"10.3390","volume":"22","author":[{"given":"Jinsoo","family":"Park","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kwangseok","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Electronic Electrical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"085007","DOI":"10.1088\/1361-6579\/ab2c9f","article-title":"Robust heart-rate estimation from facial videos using Project_ICA","volume":"40","author":"Qi","year":"2019","journal-title":"Physiol. 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