Improved Heart-Rate Measurement from Mobile Face Videos

Newtonian reaction to blood influx into the head at each heartbeat causes subtle head motion at the same frequency as the heartbeats. Thus, this head motion can be used to estimate the heart rate. Several studies have shown that heart rates can be measured accurately by tracking head motion using a...

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Main Authors: Jean-Pierre Lomaliza, Hanhoon Park
Format: Article
Language:English
Published: MDPI AG 2019-06-01
Series:Electronics
Subjects:
Online Access:https://www.mdpi.com/2079-9292/8/6/663
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spelling doaj-b6269846c9be48d3a44bfadeb1e39f552020-11-25T01:58:52ZengMDPI AGElectronics2079-92922019-06-018666310.3390/electronics8060663electronics8060663Improved Heart-Rate Measurement from Mobile Face VideosJean-Pierre Lomaliza0Hanhoon Park1Department of Electronic Engineering, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan 48513, KoreaDepartment of Electronic Engineering, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan 48513, KoreaNewtonian reaction to blood influx into the head at each heartbeat causes subtle head motion at the same frequency as the heartbeats. Thus, this head motion can be used to estimate the heart rate. Several studies have shown that heart rates can be measured accurately by tracking head motion using a desktop computer with a static camera. However, implementation of vision-based head motion tracking on smartphones demonstrated limited accuracy due to the hand-shaking problem caused by the non-static camera. The hand-shaking problem could not be handled effectively with only the frontal camera images. It also required a more accurate method to measure the periodicity of noisy signals. Therefore, this study proposes an improved head-motion-based heart-rate monitoring system using smartphones. To address the hand-shaking problem, the proposed system leverages the front and rear cameras available in most smartphones and dedicates each camera to tracking facial features that correspond to head motion and background features that correspond to hand-shaking. Then, the locations of facial features are adjusted using the average point of the background features. In addition, a correlation-based signal periodicity computation method is proposed to accurately separate the true heart-rate-related component from the head motion signal. The proposed system demonstrates improved accuracy (i.e., lower mean errors in heart-rate measurement) compared to conventional head-motion-based systems, and the accuracy is sufficient for daily heart-rate monitoring.https://www.mdpi.com/2079-9292/8/6/663heart-rate monitoringmobile face videohead motion analysishand-shaking handlinghealthcare
collection DOAJ
language English
format Article
sources DOAJ
author Jean-Pierre Lomaliza
Hanhoon Park
spellingShingle Jean-Pierre Lomaliza
Hanhoon Park
Improved Heart-Rate Measurement from Mobile Face Videos
Electronics
heart-rate monitoring
mobile face video
head motion analysis
hand-shaking handling
healthcare
author_facet Jean-Pierre Lomaliza
Hanhoon Park
author_sort Jean-Pierre Lomaliza
title Improved Heart-Rate Measurement from Mobile Face Videos
title_short Improved Heart-Rate Measurement from Mobile Face Videos
title_full Improved Heart-Rate Measurement from Mobile Face Videos
title_fullStr Improved Heart-Rate Measurement from Mobile Face Videos
title_full_unstemmed Improved Heart-Rate Measurement from Mobile Face Videos
title_sort improved heart-rate measurement from mobile face videos
publisher MDPI AG
series Electronics
issn 2079-9292
publishDate 2019-06-01
description Newtonian reaction to blood influx into the head at each heartbeat causes subtle head motion at the same frequency as the heartbeats. Thus, this head motion can be used to estimate the heart rate. Several studies have shown that heart rates can be measured accurately by tracking head motion using a desktop computer with a static camera. However, implementation of vision-based head motion tracking on smartphones demonstrated limited accuracy due to the hand-shaking problem caused by the non-static camera. The hand-shaking problem could not be handled effectively with only the frontal camera images. It also required a more accurate method to measure the periodicity of noisy signals. Therefore, this study proposes an improved head-motion-based heart-rate monitoring system using smartphones. To address the hand-shaking problem, the proposed system leverages the front and rear cameras available in most smartphones and dedicates each camera to tracking facial features that correspond to head motion and background features that correspond to hand-shaking. Then, the locations of facial features are adjusted using the average point of the background features. In addition, a correlation-based signal periodicity computation method is proposed to accurately separate the true heart-rate-related component from the head motion signal. The proposed system demonstrates improved accuracy (i.e., lower mean errors in heart-rate measurement) compared to conventional head-motion-based systems, and the accuracy is sufficient for daily heart-rate monitoring.
topic heart-rate monitoring
mobile face video
head motion analysis
hand-shaking handling
healthcare
url https://www.mdpi.com/2079-9292/8/6/663
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AT hanhoonpark improvedheartratemeasurementfrommobilefacevideos
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