Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/95604
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Wang, T | en_US |
dc.creator | Zhang, D | en_US |
dc.creator | Wang, L | en_US |
dc.creator | Zheng, Y | en_US |
dc.creator | Gu, T | en_US |
dc.creator | Dorizzi, B | en_US |
dc.creator | Zhou, X | en_US |
dc.date.accessioned | 2022-09-22T06:14:04Z | - |
dc.date.available | 2022-09-22T06:14:04Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/95604 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication T. Wang et al., "Contactless Respiration Monitoring Using Ultrasound Signal With Off-the-Shelf Audio Devices," in IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2959-2973, April 2019 is available at https://doi.org/10.1109/JIOT.2018.2877607 | en_US |
dc.subject | Acoustic sensing | en_US |
dc.subject | Contactless sensing | en_US |
dc.subject | Doppler effect | en_US |
dc.subject | Respiration detection | en_US |
dc.title | Contactless respiration monitoring using ultrasound signal with off-the-shelf audio devices | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 2959 | en_US |
dc.identifier.epage | 2973 | en_US |
dc.identifier.volume | 6 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.doi | 10.1109/JIOT.2018.2877607 | en_US |
dcterms.abstract | Recent years have witnessed advances of Internet of Things technologies and their applications to enable contactless sensing and elderly care in smart homes. Continuous and real-time respiration monitoring is one of the important applications to promote assistive living for elders during sleep and attracted wide attention in both academia and industry. Most of the existing respiration monitoring systems require expensive and specialized devices to sense chest displacement. However, chest displacement is not a direct indicator of breathing and thus false detection may often occur. In this paper, we design and implement a real-time and contactless respiration monitoring system by directly sensing the exhaled airflow from breathing using ultrasound signals with off-the-shelf speaker and microphone. Exhaled airflow from breathing can be regarded as air turbulence, which scatters the sound wave and results in Doppler effect. Our system works as an acoustic radar which transmits sound wave and detects the Doppler effect caused by breathing airflow. We mathematically model the relationship between the Doppler frequency change and the direction of breathing airflow. Based on this model, we design a minimum description length-based algorithm to effectively capture the Doppler effect caused by exhaled airflow. We conduct extensive experiments with 25 participants (7 elders, 2 young kids, and 16 adults, including 11 females and 14 males) in four different rooms. The participants take four different sleep postures (lying on one's back, on right/left side, and on one's stomach) in different positions of the bed. Experiment results show that our system achieves a median error lower than 0.3 breaths/min (2%) for respiration monitoring and can accurately identify Apnea. The results also demonstrate that the system is robust to different respiration styles (shallow, normal, and deep), respiration rate variation, ambient noise, sensing distance variation (within 0.7 m), and transmitted signal frequency variation. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE internet of things journal, Apr. 2019, v. 6, no. 2, 8502803, p. 2959-2973 | en_US |
dcterms.isPartOf | IEEE internet of things journal | en_US |
dcterms.issued | 2019-04 | - |
dc.identifier.scopus | 2-s2.0-85055690731 | - |
dc.identifier.eissn | 2327-4662 | en_US |
dc.identifier.artn | 8502803 | en_US |
dc.description.validate | 202209_bcww | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | COMP-1014 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 13052808 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Zheng_Contactless_Respiration_Monitoring.pdf | Pre-Published version | 1.43 MB | Adobe PDF | View/Open |
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