{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T11:22:15Z","timestamp":1779103335516,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2017,6,24]],"date-time":"2017-06-24T00:00:00Z","timestamp":1498262400000},"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>Subject movement and a dark environment will increase the difficulty of image-based contactless pulse rate detection. In this paper, we detected the subject\u2019s motion status based on complexion tracking and proposed a motion index (MI) to filter motion artifacts in order to increase pulse rate measurement accuracy. Additionally, we integrated the near infrared (NIR) LEDs with the adopted sensor and proposed an effective method to measure the pulse rate in a dark environment. To achieve real-time data processing, the proposed framework is constructed on a Field Programmable Gate Array (FPGA) platform. Next, the instant pulse rate and motion status are transmitted to a smartphone for remote monitoring. The experiment results showed the error of the pulse rate detection to be within \u22123.44 to +4.53 bpm under sufficient ambient light and \u22122.96 to + 4.24 bpm for night mode detection, when the moving speed is higher than 14.45 cm\/s. These results demonstrate that the proposed method can improve the robustness of image-based contactless pulse rate detection despite subject movement and a dark environment.<\/jats:p>","DOI":"10.3390\/s17071490","type":"journal-article","created":{"date-parts":[[2017,6,27]],"date-time":"2017-06-27T02:58:05Z","timestamp":1498532285000},"page":"1490","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A Real-Time Contactless Pulse Rate and Motion Status Monitoring System Based on Complexion Tracking"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6497-0282","authenticated-orcid":false,"given":"Yu-Chen","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, 10607 Taipei, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nai-Kuan","family":"Chou","sequence":"additional","affiliation":[{"name":"Department of Surgery, National Taiwan University Hospital, 10002 Taipei, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guan-You","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, 10607 Taipei, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng-Han","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, 10607 Taipei, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0630-7313","authenticated-orcid":false,"given":"Yuan-Hsiang","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, 10607 Taipei, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,24]]},"reference":[{"key":"ref_1","unstructured":"(2016, March 04). 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