对运动状态下使用耳内麦克风进行心率监测的评估

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Kayla-Jade Butkow , Ting Dang , Andrea Ferlini , Dong Ma , Yang Liu , Cecilia Mascolo
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引用次数: 0

摘要

随着入耳式可穿戴设备的普及,研究界开始研究合适的入耳式心率检测系统。心率是心血管健康和体能的关键生理指标。因此,利用可穿戴设备进行连续、可靠的心率监测近年来日益受到关注。现有的可穿戴设备心率检测系统主要依赖于光敏血压计(PPG)传感器,但这些传感器在人体运动时的性能较差。在这项工作中,我们利用闭塞效应增强耳道中的低频骨传导声音,首次研究了心率监测。我们首先使用耳内麦克风收集了七种静止活动和两种全身运动活动(即行走和跑步)下耳道中的心率感应声音。然后,我们设计了一种新颖的基于深度学习的运动伪影(MA)缓解框架来对耳内音频信号进行去噪处理,接着使用心率估计算法来提取心率。通过收集 15 名受试者在 9 项活动中的数据,我们证明了我们的端到端方法 hEARt 在静止、步行和跑步时的平均绝对误差(MAE)分别为 1.88 ± 2.89 BPM、6.83 ± 5.05 BPM 和 13.19 ± 11.37 BPM,为日常活动中使用性能良好的新型无创、经济型心率监测打开了大门。hEARt 不仅优于以前的耳内式心率监测工作,而且优于已报道的耳内式 PPG 性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.

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来源期刊
Pervasive and Mobile Computing
Pervasive and Mobile Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS
CiteScore
7.70
自引率
2.30%
发文量
80
审稿时长
68 days
期刊介绍: As envisioned by Mark Weiser as early as 1991, pervasive computing systems and services have truly become integral parts of our daily lives. Tremendous developments in a multitude of technologies ranging from personalized and embedded smart devices (e.g., smartphones, sensors, wearables, IoTs, etc.) to ubiquitous connectivity, via a variety of wireless mobile communications and cognitive networking infrastructures, to advanced computing techniques (including edge, fog and cloud) and user-friendly middleware services and platforms have significantly contributed to the unprecedented advances in pervasive and mobile computing. Cutting-edge applications and paradigms have evolved, such as cyber-physical systems and smart environments (e.g., smart city, smart energy, smart transportation, smart healthcare, etc.) that also involve human in the loop through social interactions and participatory and/or mobile crowd sensing, for example. The goal of pervasive computing systems is to improve human experience and quality of life, without explicit awareness of the underlying communications and computing technologies. The Pervasive and Mobile Computing Journal (PMC) is a high-impact, peer-reviewed technical journal that publishes high-quality scientific articles spanning theory and practice, and covering all aspects of pervasive and mobile computing and systems.
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