Changshuo Hu, Thivya Kandappu, Yang Liu, Cecilia Mascolo, Dong Ma
{"title":"BreathPro: Monitoring Breathing Mode during Running with Earables","authors":"Changshuo Hu, Thivya Kandappu, Yang Liu, Cecilia Mascolo, Dong Ma","doi":"10.1145/3659607","DOIUrl":null,"url":null,"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.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3659607","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
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.