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C-FMCW Based Contactless Respiration Detection Using Acoustic Signal

Published: 08 January 2018 Publication History

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.

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    cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
    Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 1, Issue 4
    December 2017
    1298 pages
    EISSN:2474-9567
    DOI:10.1145/3178157
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 08 January 2018
    Accepted: 01 October 2017
    Revised: 01 September 2017
    Received: 01 May 2017
    Published in IMWUT Volume 1, Issue 4

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    Author Tags

    1. Acoustic sensing
    2. Contactless Sensing
    3. Device-free Ranging
    4. Health Monitoring
    5. Respiration Detection

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    • (2025)Listen to Your Face: A Face Authentication Scheme Based on Acoustic SignalsACM Transactions on Sensor Networks10.1145/370832421:1(1-23)Online publication date: 27-Jan-2025
    • (2024)Remote Multi-Person Heart Rate Monitoring with Smart Speakers: Overcoming Separation ConstraintSensors10.3390/s2402038224:2(382)Online publication date: 8-Jan-2024
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