BreathPro: Monitoring Breathing Mode during Running with Earables

IF 3.6 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Pub Date : 2024-05-13 DOI:10.1145/3659607
Changshuo Hu, Thivya Kandappu, Yang Liu, Cecilia Mascolo, Dong Ma
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Abstract

Running is a popular and accessible form of aerobic exercise, significantly benefiting our health and wellness. By monitoring a range of running parameters with wearable devices, runners can gain a deep understanding of their running behavior, facilitating performance improvement in future runs. Among these parameters, breathing, which fuels our bodies with oxygen and expels carbon dioxide, is crucial to improving the efficiency of running. While previous studies have made substantial progress in measuring breathing rate, exploration of additional breathing monitoring during running is still lacking. In this work, we fill this gap by presenting BreathPro, the first breathing mode monitoring system for running. It leverages the in-ear microphone on earables to record breathing sounds and combines the out-ear microphone on the same device to mitigate external noises, thereby enhancing the clarity of in-ear breathing sounds. BreathPro incorporates a suite of well-designed signal processing and machine learning techniques to enable breathing mode detection with superior accuracy. We implemented BreathPro as a smartphone application and demonstrated its energy-efficient and real-time execution.
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BreathPro:使用耳机监测跑步时的呼吸模式
跑步是一种既普及又容易接受的有氧运动,对我们的健康和保健大有裨益。通过使用可穿戴设备监测一系列跑步参数,跑步者可以深入了解自己的跑步行为,从而有助于在今后的跑步中提高成绩。在这些参数中,呼吸是提高跑步效率的关键,它为我们的身体提供氧气并排出二氧化碳。虽然之前的研究在测量呼吸频率方面取得了重大进展,但对跑步过程中的其他呼吸监测的探索仍然缺乏。在这项工作中,我们推出了首个跑步呼吸模式监测系统 BreathPro,填补了这一空白。它利用耳机上的入耳式麦克风来记录呼吸声,并结合同一设备上的出耳式麦克风来减少外部噪音,从而提高入耳式呼吸声的清晰度。BreathPro 融合了一整套精心设计的信号处理和机器学习技术,能以极高的精度进行呼吸模式检测。我们将 BreathPro 作为一款智能手机应用进行了开发,并展示了其高能效和实时性。
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来源期刊
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Computer Science-Computer Networks and Communications
CiteScore
9.10
自引率
0.00%
发文量
154
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