Heart: Motion-resilient heart rate monitoring with in-ear microphones
With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate (HR) detection systems. HR is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable HR monitoring with wearable devices has therefore...
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2023
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sg-smu-ink.sis_research-92812023-11-10T08:38:59Z Heart: Motion-resilient heart rate monitoring with in-ear microphones BUTKOW, Kayla-Jade DANG, Ting FERLINI, Andrea MA, Dong MASCOLO, With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate (HR) detection systems. HR is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable HR monitoring with wearable devices has therefore gained increasing attention in recent years. Existing HR 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 HR monitoring. We first collected HR-induced sounds in the ear canal leveraging an in-ear microphone under stationary and three different activities (i.e., walking, running, and speaking). Then, we devised a novel deep learning based motion artefact (MA) mitigation framework to denoise the in-ear audio signals, followed by an HR estimation algorithm to extract HR. With data collected from 20 subjects over four activities, we demonstrate that hEARt, our end-To-end approach, achieves a mean absolute error (MAE) of 3.02 2.97 BPM, 8.12 {6.74} BPM, 11.23 {9.20} BPM and 9.39 {6.97} BPM for stationary, walking, running and speaking, respectively, opening the door to a new non-invasive and affordable HR monitoring with usable performance for daily activities. Not only does hEARt outperform previous in-ear HR monitoring work, but it outperforms reported in-ear PPG performance 2023-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8278 info:doi/10.1109/PERCOM56429.2023.10099317 https://ink.library.smu.edu.sg/context/sis_research/article/9281/viewcontent/hEART_Percom23.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 Detection system Ear canal Earable Heart-rate Heart-rate detection Heart-rate monitoring In-ear audio Motion artifact Performance Research communities Software Engineering |
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Detection system Ear canal Earable Heart-rate Heart-rate detection Heart-rate monitoring In-ear audio Motion artifact Performance Research communities Software Engineering BUTKOW, Kayla-Jade DANG, Ting FERLINI, Andrea MA, Dong MASCOLO, Heart: Motion-resilient heart rate monitoring with in-ear microphones |
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With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate (HR) detection systems. HR is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable HR monitoring with wearable devices has therefore gained increasing attention in recent years. Existing HR 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 HR monitoring. We first collected HR-induced sounds in the ear canal leveraging an in-ear microphone under stationary and three different activities (i.e., walking, running, and speaking). Then, we devised a novel deep learning based motion artefact (MA) mitigation framework to denoise the in-ear audio signals, followed by an HR estimation algorithm to extract HR. With data collected from 20 subjects over four activities, we demonstrate that hEARt, our end-To-end approach, achieves a mean absolute error (MAE) of 3.02 2.97 BPM, 8.12 {6.74} BPM, 11.23 {9.20} BPM and 9.39 {6.97} BPM for stationary, walking, running and speaking, respectively, opening the door to a new non-invasive and affordable HR monitoring with usable performance for daily activities. Not only does hEARt outperform previous in-ear HR monitoring work, but it outperforms reported in-ear PPG performance |
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BUTKOW, Kayla-Jade DANG, Ting FERLINI, Andrea MA, Dong MASCOLO, |
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BUTKOW, Kayla-Jade DANG, Ting FERLINI, Andrea MA, Dong MASCOLO, |
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BUTKOW, Kayla-Jade |
title |
Heart: Motion-resilient heart rate monitoring with in-ear microphones |
title_short |
Heart: Motion-resilient heart rate monitoring with in-ear microphones |
title_full |
Heart: Motion-resilient heart rate monitoring with in-ear microphones |
title_fullStr |
Heart: Motion-resilient heart rate monitoring with in-ear microphones |
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Heart: Motion-resilient heart rate monitoring with in-ear microphones |
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heart: motion-resilient heart rate monitoring with in-ear microphones |
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Institutional Knowledge at Singapore Management University |
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2023 |
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https://ink.library.smu.edu.sg/sis_research/8278 https://ink.library.smu.edu.sg/context/sis_research/article/9281/viewcontent/hEART_Percom23.pdf |
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