VitaMon: Measuring heart rate variability using smartphone front camera

We present VitaMon, a mobile sensing system that can measure the inter-heartbeat interval (IBI) from the facial video captured by a commodity smartphone's front camera. The continuous IBI measurement is used to compute heart rate variability (HRV), one of the most important markers of the auton...

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Main Authors: HUYNH, Sinh, BALAN, Rajesh Krishna, KO, JeongGil, LEE, Youngki
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Language:English
Published: Institutional Knowledge at Singapore Management University 2019
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Online Access:https://ink.library.smu.edu.sg/sis_research/4933
https://ink.library.smu.edu.sg/context/sis_research/article/5936/viewcontent/VitaMon_2019_pv_oa.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-59362020-04-09T09:13:02Z VitaMon: Measuring heart rate variability using smartphone front camera HUYNH, Sinh BALAN, Rajesh Krishna KO, JeongGil LEE, Youngki We present VitaMon, a mobile sensing system that can measure the inter-heartbeat interval (IBI) from the facial video captured by a commodity smartphone's front camera. The continuous IBI measurement is used to compute heart rate variability (HRV), one of the most important markers of the autonomic nervous system (ANS) regulation. The underlying idea of VitaMon is that video recording of human face contains multiple cardiovascular pulse signals with different phase shift. Our measurement on 10 participants shows the significant time delay (36.79 ms) between the pulse signals measured at the jaw region and forehead region. VitaMon leverages deep neural network models to extract both spatial and temporal information of the video to reconstruct a pulse waveform signal that is optimized for estimating IBI. We evaluated VitaMon with a dataset collected from 30 participants under various conditions involving different light intensity levels and motion artifacts. With the 15 fps video input (66.67 ms time resolution), VitaMon can measure IBI with an average error of 14.26 ms and 21.65 ms using personal and general model respectively. HRV features including geometry Poincare plot, time- and frequency-domain features extracted from the IBI measurement all have high correlation with the reference signal. 2019-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4933 info:doi/10.1145/3356250.3360036 https://ink.library.smu.edu.sg/context/sis_research/article/5936/viewcontent/VitaMon_2019_pv_oa.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Heart Rate Variability Photoplethysmography (PPG) Remote PPG Mobile Sensing Computer Sciences Health Information Technology Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Heart Rate Variability
Photoplethysmography (PPG)
Remote PPG
Mobile Sensing
Computer Sciences
Health Information Technology
Software Engineering
spellingShingle Heart Rate Variability
Photoplethysmography (PPG)
Remote PPG
Mobile Sensing
Computer Sciences
Health Information Technology
Software Engineering
HUYNH, Sinh
BALAN, Rajesh Krishna
KO, JeongGil
LEE, Youngki
VitaMon: Measuring heart rate variability using smartphone front camera
description We present VitaMon, a mobile sensing system that can measure the inter-heartbeat interval (IBI) from the facial video captured by a commodity smartphone's front camera. The continuous IBI measurement is used to compute heart rate variability (HRV), one of the most important markers of the autonomic nervous system (ANS) regulation. The underlying idea of VitaMon is that video recording of human face contains multiple cardiovascular pulse signals with different phase shift. Our measurement on 10 participants shows the significant time delay (36.79 ms) between the pulse signals measured at the jaw region and forehead region. VitaMon leverages deep neural network models to extract both spatial and temporal information of the video to reconstruct a pulse waveform signal that is optimized for estimating IBI. We evaluated VitaMon with a dataset collected from 30 participants under various conditions involving different light intensity levels and motion artifacts. With the 15 fps video input (66.67 ms time resolution), VitaMon can measure IBI with an average error of 14.26 ms and 21.65 ms using personal and general model respectively. HRV features including geometry Poincare plot, time- and frequency-domain features extracted from the IBI measurement all have high correlation with the reference signal.
format text
author HUYNH, Sinh
BALAN, Rajesh Krishna
KO, JeongGil
LEE, Youngki
author_facet HUYNH, Sinh
BALAN, Rajesh Krishna
KO, JeongGil
LEE, Youngki
author_sort HUYNH, Sinh
title VitaMon: Measuring heart rate variability using smartphone front camera
title_short VitaMon: Measuring heart rate variability using smartphone front camera
title_full VitaMon: Measuring heart rate variability using smartphone front camera
title_fullStr VitaMon: Measuring heart rate variability using smartphone front camera
title_full_unstemmed VitaMon: Measuring heart rate variability using smartphone front camera
title_sort vitamon: measuring heart rate variability using smartphone front camera
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/sis_research/4933
https://ink.library.smu.edu.sg/context/sis_research/article/5936/viewcontent/VitaMon_2019_pv_oa.pdf
_version_ 1770575099192147968