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RFID-Based Respiration Monitoring Under Daily Body Motion Disturbances.

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Title: RFID-Based Respiration Monitoring Under Daily Body Motion Disturbances.
Authors: Zhao, Chuanxin1 (AUTHOR) zcxonline@126.com, Ding, Haotian1 (AUTHOR), Xu, Zhiqiang1 (AUTHOR), Chen, Siguang2 (AUTHOR)
Source: International Journal of Human-Computer Interaction. Jun2025, Vol. 41 Issue 12, p7798-7814. 17p.
Subjects: Ventilation monitoring, Respiratory organs, Wearable technology, Shoulder, Signals & signaling
Abstract: Traditional respiratory monitoring methods typically use wearable sensors. In recent years, Radio Frequency Identification (RFID) tags have emerged as potential respiratory monitoring devices. However, existing research only detects respiration under static or simple body movements such as forward and backward motions, which limits the availability of RFID. In this article, an RFID-based respiratory monitoring system under body motion disturbances is proposed. In order to eliminate the effect of body movement on the signal, reference tags are placed on the user's shoulders. The phase between different tags is calibrated and aligned to remove the discreteness. Then, a sliding window peak-seeking algorithm is designed to calculate respiratory rate over time. The system measures respiratory rate during daily exercise and maintains an accuracy of no less than 86.12%, even when influenced by lying down and turning. Additionally, the system can analyze estimated respiratory rates and intervals to provide alert information. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: RFID-Based Respiration Monitoring Under Daily Body Motion Disturbances.
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  Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Chuanxin%22">Zhao, Chuanxin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zcxonline@126.com</i><br /><searchLink fieldCode="AR" term="%22Ding%2C+Haotian%22">Ding, Haotian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Zhiqiang%22">Xu, Zhiqiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Siguang%22">Chen, Siguang</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Jun2025, Vol. 41 Issue 12, p7798-7814. 17p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Ventilation+monitoring%22">Ventilation monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Respiratory+organs%22">Respiratory organs</searchLink><br /><searchLink fieldCode="DE" term="%22Wearable+technology%22">Wearable technology</searchLink><br /><searchLink fieldCode="DE" term="%22Shoulder%22">Shoulder</searchLink><br /><searchLink fieldCode="DE" term="%22Signals+%26+signaling%22">Signals & signaling</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Traditional respiratory monitoring methods typically use wearable sensors. In recent years, Radio Frequency Identification (RFID) tags have emerged as potential respiratory monitoring devices. However, existing research only detects respiration under static or simple body movements such as forward and backward motions, which limits the availability of RFID. In this article, an RFID-based respiratory monitoring system under body motion disturbances is proposed. In order to eliminate the effect of body movement on the signal, reference tags are placed on the user's shoulders. The phase between different tags is calibrated and aligned to remove the discreteness. Then, a sliding window peak-seeking algorithm is designed to calculate respiratory rate over time. The system measures respiratory rate during daily exercise and maintains an accuracy of no less than 86.12%, even when influenced by lying down and turning. Additionally, the system can analyze estimated respiratory rates and intervals to provide alert information. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/10447318.2024.2400402
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 17
        StartPage: 7798
    Subjects:
      – SubjectFull: Ventilation monitoring
        Type: general
      – SubjectFull: Respiratory organs
        Type: general
      – SubjectFull: Wearable technology
        Type: general
      – SubjectFull: Shoulder
        Type: general
      – SubjectFull: Signals & signaling
        Type: general
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      – TitleFull: RFID-Based Respiration Monitoring Under Daily Body Motion Disturbances.
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            NameFull: Zhao, Chuanxin
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            NameFull: Ding, Haotian
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            NameFull: Xu, Zhiqiang
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            – D: 15
              M: 06
              Text: Jun2025
              Type: published
              Y: 2025
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            – TitleFull: International Journal of Human-Computer Interaction
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