C-FMCW Based Contactless Respiration Detection Using Acoustic Signal

Author:

Wang Tianben1,Zhang Daqing2,Zheng Yuanqing3,Gu Tao4,Zhou Xingshe1,Dorizzi Bernadette2

Affiliation:

1. School of Computer Science, Northwestern Polytechnical University, Xi'an Shaanxi, China

2. Institut Mines-Télécom/Télécom, rue Charles Fourier, Evry Cedex, France

3. The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

4. School of Computer Science and IT, RMIT University, Australia

Abstract

Recent advances in ubiquitous sensing technologies have exploited various approaches to monitoring vital signs. One of the vital signs is human respiration which typically requires reliable monitoring with low error rate in practice. Previous works in respiration monitoring however either incur high cost or suffer from poor error rate. In this paper, we propose a Correlation based Frequency Modulated Continuous Wave method (C-FMCW) which is able to achieve high ranging resolution. Based on C-FMCW, we present the design and implementation of an audio-based highly-accurate system for human respiration monitoring, leveraging on commodity speaker and microphone widely available in home environments. The basic idea behind the audio-based method is that when a user is close to a pair of speaker and microphone, body movement during respiration causes periodic audio signal changes, which can be extracted to obtain the respiration rate. However, several technical challenges exist when applying C-FMCW to detect respiration with commodity acoustic devices. First, the sampling frequency offset between speakers and microphones if not being corrected properly would cause high ranging errors. Second, the uncertain starting time difference between the speaker and microphone varies over time. Moreover, due to multipath effect, weak periodic components due to respiration can easily be overwhelmed by strong static components in practice. To address those challenges, we 1) propose an algorithm to compensate dynamically acoustic signal and counteract the offset between speaker and microphone; 2) co-locate speaker and microphone and use the received signal without reflection (self-interference) as a reference to eliminate the starting time difference; and 3) leverage the periodicity of respiration to extract weak periodic components with autocorrelation. Extensive experimental results show that our system detects respiration in real environments with the median error lower than 0.35 breaths/min, outperforming the state-of-the-arts.

Funder

Hong Kong PolyU under Grant G-YBMT

National Natural Science Foundation of China

Chinese Scholarship Council Program

ong Kong ECS under Grant PolyU

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction

Reference40 articles.

1. Respiratory rate: the neglected vital sign

2. Respiratory rate and breathing pattern;Drost Nicole A.;McMaster University Medical Journal,2013

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