An evaluation of heart rate monitoring with in-ear microphones under motion

With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate detection systems. Heart rate is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable heart rate monitoring with wearable devices ha...

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Main Authors: BUTKOW, Kayla-Jade, DANG, Ting, FERLINI, Andrea, MA, Dong, LIU, Yang, MASCOLO, Cecilia
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/8714
https://ink.library.smu.edu.sg/context/sis_research/article/9717/viewcontent/1_s2.0_S1574119224000397_pvoa_cc_by.pdf
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spelling sg-smu-ink.sis_research-97172024-04-04T07:43:37Z An evaluation of heart rate monitoring with in-ear microphones under motion BUTKOW, Kayla-Jade DANG, Ting FERLINI, Andrea MA, Dong LIU, Yang MASCOLO, Cecilia With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate detection systems. Heart rate is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable heart rate monitoring with wearable devices has therefore gained increasing attention in recent years. Existing heart rate detection systems in wearables mainly rely on photoplethysmography (PPG) sensors, however, these are notorious for poor performance in the presence of human motion. In this work, leveraging the occlusion effect that enhances low-frequency bone-conducted sounds in the ear canal, we investigate for the first time in-ear audio-based motion-resilient heart rate monitoring. We first collected heart rate-induced sounds in the ear canal using an in-ear microphone under seven stationary activities and two full-body motion activities (i.e., walking, and running). Then, we devised a novel deep learning based motion artefact (MA) mitigation framework to denoise the in-ear audio signals, followed by a heart rate estimation algorithm to extract heart rate. With data collected from 15 subjects over nine activities, we demonstrate that hEARt, our end-to-end approach, achieves a mean absolute error (MAE) of 1.88 ± 2.89 BPM, 6.83 ± 5.05 BPM, and 13.19 ± 11.37 BPM for stationary, walking, and running, respectively, opening the door to a new non-invasive and affordable heart rate monitoring with useable performance for daily activities. Not only does hEARt outperform previous in-ear heart rate monitoring work, but it outperforms reported in-ear PPG performance. 2024-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8714 info:doi/10.1016/j.pmcj.2024.101913 https://ink.library.smu.edu.sg/context/sis_research/article/9717/viewcontent/1_s2.0_S1574119224000397_pvoa_cc_by.pdf http://creativecommons.org/licenses/by/3.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Earable Heart rate In-ear audio Motion artefact 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 Earable
Heart rate
In-ear audio
Motion artefact
Health Information Technology
Software Engineering
spellingShingle Earable
Heart rate
In-ear audio
Motion artefact
Health Information Technology
Software Engineering
BUTKOW, Kayla-Jade
DANG, Ting
FERLINI, Andrea
MA, Dong
LIU, Yang
MASCOLO, Cecilia
An evaluation of heart rate monitoring with in-ear microphones under motion
description With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate detection systems. Heart rate is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable heart rate monitoring with wearable devices has therefore gained increasing attention in recent years. Existing heart rate detection systems in wearables mainly rely on photoplethysmography (PPG) sensors, however, these are notorious for poor performance in the presence of human motion. In this work, leveraging the occlusion effect that enhances low-frequency bone-conducted sounds in the ear canal, we investigate for the first time in-ear audio-based motion-resilient heart rate monitoring. We first collected heart rate-induced sounds in the ear canal using an in-ear microphone under seven stationary activities and two full-body motion activities (i.e., walking, and running). Then, we devised a novel deep learning based motion artefact (MA) mitigation framework to denoise the in-ear audio signals, followed by a heart rate estimation algorithm to extract heart rate. With data collected from 15 subjects over nine activities, we demonstrate that hEARt, our end-to-end approach, achieves a mean absolute error (MAE) of 1.88 ± 2.89 BPM, 6.83 ± 5.05 BPM, and 13.19 ± 11.37 BPM for stationary, walking, and running, respectively, opening the door to a new non-invasive and affordable heart rate monitoring with useable performance for daily activities. Not only does hEARt outperform previous in-ear heart rate monitoring work, but it outperforms reported in-ear PPG performance.
format text
author BUTKOW, Kayla-Jade
DANG, Ting
FERLINI, Andrea
MA, Dong
LIU, Yang
MASCOLO, Cecilia
author_facet BUTKOW, Kayla-Jade
DANG, Ting
FERLINI, Andrea
MA, Dong
LIU, Yang
MASCOLO, Cecilia
author_sort BUTKOW, Kayla-Jade
title An evaluation of heart rate monitoring with in-ear microphones under motion
title_short An evaluation of heart rate monitoring with in-ear microphones under motion
title_full An evaluation of heart rate monitoring with in-ear microphones under motion
title_fullStr An evaluation of heart rate monitoring with in-ear microphones under motion
title_full_unstemmed An evaluation of heart rate monitoring with in-ear microphones under motion
title_sort evaluation of heart rate monitoring with in-ear microphones under motion
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/8714
https://ink.library.smu.edu.sg/context/sis_research/article/9717/viewcontent/1_s2.0_S1574119224000397_pvoa_cc_by.pdf
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