Heartbeat and respiration monitoring based on FBG sensor network

Zhichao, Liu; Xi, Zhang; Taoping, Shao; Jiahe, Meng

doi:10.1016/j.yofte.2023.103561

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Measuring the resonant wavelengths of fiber Bragg grating (FBG) sensors, finding their fingerprints or classifying FBGs itself in optical sensing systems require denoising the acquired FBG spectral peaks [1][2][3][4][5][6][7] Meanwhile, all important properties of FBG spectral peaks have to be preserved.…”

Section: Introductionmentioning

confidence: 99%

Impact of Reducing Statistically Small Population Sampling on 2 Threshold Detection in FBG Optical Sensing

Cibira,

Glesk,

Dubovan

et al. 2024

Preprint

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Statistical denoising techniques have been well studied, however many challenges still remain. This 9 article focuses on determining the least necessary number of significant samples in a statistical pop-10 ulation sampling, while minimizing the impact on the statistical detection of the fiber Bragg grating 11 (FBG) power spectra. This is achieved by using Bayesian decision making, small population statis-12 tics while excluding false detection. First, we investigate the impact of two-sided sliding window 13 technique applied near the cell under the test. The window size varies from 8 to 60 samples. The 14 considered maximum size of population sampling is connected with the number of wavelength 15 samples within the bandwidth of the symmetrical FBG power spectra peaks. Regarding the sym-16 metry of FBG power spectra peaks and the possible reduction of computational demands, we inves-17 tigate the impact of a one-sided sliding window. This enables to achieve less processing latency for 18 real time applications thus brings benefits to FBG-based fiber optical sensing. Both, two- and one-19 sided small population sampling techniques are then experimentally verified. Detection results are 20 analyzed in depth via calculating detection thresholds by using the small size window sliding along-21 side the waveband. From results achieved, it was found that the normality three-sigma rule does 22 not need to be followed while using the small population sampling. Experimental demonstrations 23 and analyses showed that the proposed concept of denoising and statistical threshold detection 24 based on the sliding window does not depend on the prior knowledge of probability distribution 25 functions of the FBG power spectra peaks level and background noise. We have also found that the 26 thresholds’ adaptability mainly depends on the statistical numerical characteristics of the above ad-27 ditive mixture of the FBG power spectra peaks signals and background noise.

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Section: Introductionmentioning

confidence: 99%

Impact of Reducing Statistically Small Population Sampling on 2 Threshold Detection in FBG Optical Sensing

Cibira,

Glesk,

Dubovan

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Measuring the resonant wavelengths of fiber Bragg grating (FBG) sensors, finding their fingerprints, or classifying FBGs themselves in optical sensing systems require denoising the acquired FBG spectral peaks [1][2][3][4][5][6][7]. Meanwhile, all important properties of FBG spectral peaks have to be preserved.…”

Section: Introductionmentioning

confidence: 99%

Impact of Reducing Statistically Small Population Sampling on Threshold Detection in FBG Optical Sensing

Cibira,

Glesk,

Dubovan

et al. 2024

Sensors

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Many techniques have been studied for recovering information from shared media such as optical fiber that carries different types of communication, sensing, and data streaming. This article focuses on a simple method for retrieving the targeted information with the least necessary number of significant samples when using statistical population sampling. Here, the focus is on the statistical denoising and detection of the fiber Bragg grating (FBG) power spectra. The impact of the two-sided and one-sided sliding window technique is investigated. The size of the window is varied up to one-half of the symmetrical FBG power spectra bandwidth. Both, two- and one-sided small population sampling techniques were experimentally investigated. We found that the shorter sliding window delivered less processing latency, which would benefit real-time applications. The calculated detection thresholds were used for in-depth analysis of the data we obtained. It was found that the normality three-sigma rule does not need to be followed when a small population sampling is used. Experimental demonstrations and analyses also showed that novel denoising and statistical threshold detection do not depend on prior knowledge of the probability distribution functions that describe the FBG power spectra peaks and background noise. We have demonstrated that the detection thresholds’ adaptability strongly depends on the mean and standard deviation values of the small population sampling.

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“…According to the demodulation method, the optical fiber respiratory sensors can be mainly divided into Fiber Bragg Grating (FBG) type and interferometric type. FBG based respiratory sensors are usually fixed on clothes, textiles, or buried in the mattress or cushion [ 4 – 6 ]. Human respiration will cause the periodic strain variation of the fiber grating, and human respiratory signals can be obtained by detecting the central wavelength shift.…”

Section: Introductionmentioning

confidence: 99%

Optical fiber sensor for wearable and accurate human respiratory monitoring

Shao,

Yuan,

Wang

et al. 2024

Biomed. Opt. Express

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Accurate respiratory monitoring is of great significance in assessing and analyzing physical health, and preventing respiratory diseases. The recently emerged wearable respiratory sensors are confronted with the challenges such as complex fabrication processes, limited accuracy, and stringent wearing requirements. An optical fiber sensor for accurate human respiratory monitoring is proposed and experimentally verified. The sensor head is composed of a piece of seven core fiber sandwiched between two single-mode fibers by two fiber bitapers, which is embedded in a textile sheet and freely worn on the upper body. An efficient signal demodulation system is set up to acquire the respiratory signal, while Fourier transform (FFT) and short-time Fourier transform (STFT) methods are used to analyze the measured signal. Six volunteers are invited to perform the respiratory experiment, and the experimental results demonstrate that the sensor can accurately detect and distinguish respiratory signals under different humans, different states (normal, slow, fast), different body parts (abdomen, chest, back), different postures (standing, sitting, lying), and irregular respiration. The Pearson correlation coefficient of the sensor is higher than 0.9, which is consistent with commercial respiratory sensor. Meanwhile, the instability of the sensor is 0.003 Hz for the same volunteer in 6 months. The sensor has the advantages of high sensitivity, good stability and wearing comfort, showing good potential in healthcare applications.

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Heartbeat and respiration monitoring based on FBG sensor network

Cited by 8 publications

References 25 publications

Impact of Reducing Statistically Small Population Sampling on 2 Threshold Detection in FBG Optical Sensing

Impact of Reducing Statistically Small Population Sampling on 2 Threshold Detection in FBG Optical Sensing

Impact of Reducing Statistically Small Population Sampling on Threshold Detection in FBG Optical Sensing

Optical fiber sensor for wearable and accurate human respiratory monitoring

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