RFID-Based Respiration Monitoring Under Daily Body Motion Disturbances.
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| Title: | RFID-Based Respiration Monitoring Under Daily Body Motion Disturbances. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 10447318 |
| DOI: | 10.1080/10447318.2024.2400402 |
