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딥러닝 기반의 모바일 얼굴 영상을 이용한 실시간 심박수 측정 시스템Deep Learning-based Real-time Heart Rate Measurement System Using Mobile Facial Videos

Other Titles
Deep Learning-based Real-time Heart Rate Measurement System Using Mobile Facial Videos
Authors
지예림임서연박소연김상하동서연
Issue Date
Nov-2021
Publisher
한국멀티미디어학회
Keywords
Remote Photoplethysmography; Heart Rate; Deep Learning; Mobile Application; Healthcare system
Citation
멀티미디어학회논문지, v.24, no.11, pp.1481 - 1491
Journal Title
멀티미디어학회논문지
Volume
24
Number
11
Start Page
1481
End Page
1491
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/146114
ISSN
1229-7771
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.
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ICT융합공학부 (IT공학전공)
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