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Volume 40 Issue 1
March  2025
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Yi EZ, Niu K, Zhang FS et al. Multi-person respiration monitoring leveraging commodity Wi-Fi devices. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 40(1): 229−251, Jan. 2025. DOI: 10.1007/s11390-023-2722-z
Citation: Yi EZ, Niu K, Zhang FS et al. Multi-person respiration monitoring leveraging commodity Wi-Fi devices. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 40(1): 229−251, Jan. 2025. DOI: 10.1007/s11390-023-2722-z
shu

Multi-Person Respiration Monitoring Leveraging Commodity Wi-Fi Devices

  • En-Ze Yi1,  (伊恩泽) , 
  • Kai Niu1, 2,  (牛凯) Member, CCF, ACM, 
  • Fu-Sang Zhang3,  (张扶桑) Senior Member, CCF, Member, ACM, 
  • Rui-Yang Gao1,  (高睿杨) Member, CCF, 
  • Jun Luo4,  (罗骏) Senior Member, IEEE, Member, ACM, 
  • Da-Qing Zhang1, 5, ,  (张大庆) Fellow, IEEE, Distinguished Member, CCF, Member, ACM
  • 1.

    School of Computer Science, Peking University, Beijing 100871, China

  • 2.

    Beijing Xiaomi Mobile Software Co., Ltd., Beijing 100085, China

  • 3.

    State Key Laboratory of Computer Sciences, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China

  • 4.

    School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore

  • 5.

    Telecom SudParis, Institut Polytechnique de Paris, Paris 91000, France

Funds: This work was partially supported by the National Natural Science Foundation of China A3 Foresight Program under Grant No. 62061146001, the Peking University (PKU)-Nanyang Technological University (NTU) Collaboration Project, the Project funded by China Postdoctoral Science Foundation under Grant No. 2021TQ0048, the National Natural Science Foundation of China under Grant No.62172394, the Beijing Natural Science Foundation under Grant No. L223034, the Beijing Nova Program, and the Youth Innovation Promotion Association of Chinese Academy of Sciences under Grant No. 2020109.
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  • Author Bio:

    En-Ze Yi received his B.E. degree in computer science and technology from Northeastern University, Shenyang, in 2017, and his Ph.D. degree in computer software and theory from School of Computer Science, Peking University, Beijing, in 2024. His research interests include mobile crowd-sensing and ubiquitous computing

    Kai Niu received his M.E. degree in computer technology and B.E. degree in computer science and technology from the School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, in 2013 and 2016, respectively, and his Ph.D. degree in computer software and theory from the School of Computer Science, Peking University, Beijing, in 2021. His current research interests include ubiquitous computing, wireless sensing, and autonomous driving

    Fu-Sang Zhang received his M.S. and Ph.D. degrees in computer science from the Institute of Software, Chinese Academy of Sciences, Beijing, in 2013 and 2017, respectively. He is currently a professor with the Institute of Software, Chinese Academy of Sciences, Beijing. His current research interests include mobile and pervasive computing, ad hoc network, and wireless contactless sensing

    Rui-Yang Gao received his B.E. degree in computer science and technology from Shandong University, Jinan, in 2016, and his Ph.D. degree in computer software and theory from School of Computer Science, Peking University, Beijing, in 2023. His research interests include mobile crowd-sensing and ubiquitous computing

    Jun Luo received his B.S. and M.S. degrees in electrical engineering from Tsinghua University, Beijing, in 1997 and 2000, respectively, and his Ph.D. degree in computer science from EPFL (Swiss Federal Institute of Technology in Lausanne), Lausanne, in 2006. In 2008, he joined the faculty of the School of Computer Science and Engineering, Nanyang Technological University, Singapore, where he is currently an associate professor. His research interests include mobile and pervasive computing, wireless networking, machine learning, and computer vision, as well as applied operations research

    Da-Qing Zhang received his Ph.D. degree from the University of Rome “La Sapienza”, Rome, in 1996. He is a chair professor with the School of Computer Science, Peking University, Beijing, and Telecom SudParis, Palaiseau. His current research interests include context-aware computing, urban computing, mobile computing, big data analytics, and pervasive elderly care

  • Corresponding author:

    dqzhang@sei.pku.edu.cn

  • Received Date: August 04, 2022
  • Accepted Date: April 10, 2023
    Abstract

  • Monitoring respiration is an important component of personal health care. Though recent developments in Wi-Fi sensing offer a potential tool to achieve contact-free respiration monitoring, existing proposals for Wi-Fi-based multi-person respiration sensing mainly extract individual’s respiration rate in the frequency domain using the fast Fourier transform (FFT) or multiple signal classification (MUSIC) method, leading to the following limitations: 1) largely ineffective in recovering breaths of multiple persons from received mixed signals and in differentiating individual breaths, 2) unable to acquire the time-varying respiration pattern when the subject has respiratory abnormity, such as apnea and changing respiration rates, and 3) difficult to identify the real number of subjects when multiple subjects share the same or similar respiration rates. To address these issues, we propose Wi-Fi-enabled MUlti-person SEnsing (WiMUSE) as a signal processing pipeline to perform respiration monitoring for multiple persons simultaneously. Essentially, as a pioneering time domain approach, WiMUSE models the mixed signals of multi-person respiration as a linear superposition of multiple waveforms, so as to form a blind source separation (BSS) problem. The effective separation of the signal sources (respiratory waveforms) further enables us to quantify the differences in the respiratory waveform patterns of multiple subjects, and thus to identify the number of subjects along with their respective respiration waveforms. We implement WiMUSE on commodity Wi-Fi devices and conduct extensive experiments to demonstrate that, compared with the approaches based on the FFT or MUSIC method, 90% error of respiration rate can be reduced by more than 60%.

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