Optical Fiber Microvibration Sensor Design for Wearable Multipoint Breathing Monitoring

Breathing is an important physiological indicator of human health. In this article, a wearable compact single-mode-no core-single-mode fiber (SNCS) optical microvibration sensor-based breathing monitoring system is proposed. First, piezoelectric vibration experiments were conducted, focusing on low-...

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Bibliographic Details
Published in:IEEE sensors journal 2024-11, Vol.24 (21), p.34471-34480
Main Authors: Wang, Fang, Mu, Fanyu, Zhao, Xinyi, Wang, Xu, Liu, Yingfang, Qin, Xiaozhi, Jia, Shuran, Yang, Haoqiang, Liu, Yufang
Format: Article
Language:English
Subjects:
Biomedical monitoring
Breathing
Breathing monitoring
Frequency measurement
Monitoring
multipoint
optical fiber microvibration sensor (FMVS)
Optical fiber sensors
Optical fibers
Piezoelectricity
Recovery time
Respiratory rate
Sensors
Vibration measurement
Vibration monitoring
Vibrations
wearable
Wearable technology
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Summary:Breathing is an important physiological indicator of human health. In this article, a wearable compact single-mode-no core-single-mode fiber (SNCS) optical microvibration sensor-based breathing monitoring system is proposed. First, piezoelectric vibration experiments were conducted, focusing on low-frequency signals to verify the feasibility of optical fiber microvibration sensor (FMVS) for breathing monitoring and measured frequencies reached to 0.1 Hz. The piezoelectric vibration frequency is fitted to the measured frequency with a slope of 0.99772 and correlation of 0.99879. Multipoint wearable breathing monitoring experiments on the nose and abdomen were conducted. It is demonstrated that breathing states could be identified by details, such as respiratory rate and exhale and inhale time between three normal and four irregular breathing states. Real-time and precise breath detection are ensured by the quick response time (0.11 s) and recovery time (0.14 s) of the sensor. The proposed sensor is repeatable, responsive, comfortable to wear, recognizes different breathing states, and can be used for the prediction of obstructive sleep apnea (OSA) syndrome and posttreatment rehabilitation monitoring, which has a promising medical application.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3454066