딥러닝 기반의 모바일 얼굴 영상을 이용한 실시간 심박수 측정 시스템
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 지예림 | - |
dc.contributor.author | 임서연 | - |
dc.contributor.author | 박소연 | - |
dc.contributor.author | 김상하 | - |
dc.contributor.author | 동서연 | - |
dc.date.accessioned | 2022-04-19T08:48:18Z | - |
dc.date.available | 2022-04-19T08:48:18Z | - |
dc.date.issued | 2021-11 | - |
dc.identifier.issn | 1229-7771 | - |
dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/146114 | - |
dc.description.abstract | Since most biosignals rely on contact-based measurement, there is still a problem in that it is hard to provide convenience to users by applying them to daily life. In this paper, we present a mobile application for estimating heart rate based on a deep learning model. The proposed application measures heart rate by capturing real-time face images in a non-contact manner. We trained a three-dimensional convolutional neural network to predict photoplethysmography (PPG) from face images. The face images used for training were taken in various movements and situations. To evaluate the performance of the proposed system, we used a pulse oximeter to measure a ground truth PPG. As a result, the deviation of the calculated root means square error between the heart rate from remote PPG measured by the proposed system and the heart rate from the ground truth was about 1.14, showing no significant difference. Our findings suggest that heart rate measurement by mobile applications is accurate enough to help manage health during daily life. | - |
dc.format.extent | 11 | - |
dc.language | 한국어 | - |
dc.language.iso | KOR | - |
dc.publisher | 한국멀티미디어학회 | - |
dc.title | 딥러닝 기반의 모바일 얼굴 영상을 이용한 실시간 심박수 측정 시스템 | - |
dc.title.alternative | Deep Learning-based Real-time Heart Rate Measurement System Using Mobile Facial Videos | - |
dc.type | Article | - |
dc.publisher.location | 대한민국 | - |
dc.identifier.bibliographicCitation | 멀티미디어학회논문지, v.24, no.11, pp 1481 - 1491 | - |
dc.citation.title | 멀티미디어학회논문지 | - |
dc.citation.volume | 24 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 1481 | - |
dc.citation.endPage | 1491 | - |
dc.identifier.kciid | ART002779637 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Remote Photoplethysmography | - |
dc.subject.keywordAuthor | Heart Rate | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | Mobile Application | - |
dc.subject.keywordAuthor | Healthcare system | - |
dc.identifier.url | https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE10667432 | - |
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