Non-Invasive Measurement of Vital Signs Based on Seven-Core Fiber Interferometer

Chen, Weijuan; Zhang, Yi; Yang, Huicheng; Qiu, Yishen; Li, Hui; Chen, Zhihao; Chen, Yu

doi:10.1109/jsen.2021.3061443

Cited by 22 publications

(18 citation statements)

References 26 publications

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“…It is the description of the small displacements of the human body caused by heart activities [16]. The rhythm of BCG is consistent with ECG, and the measured heart rate extracted from BCG signal agrees well with the commercial physiologic device [20]. Wang et al reported that the measured values of BCG have no statistically significant differences compared with that of ECG in the time domain, frequency domain, and non-linear indicators in a calm state [21], which shows that BCG signal is a reliable method to extract heart rate.…”

Section: Descriptions Characterssupporting

confidence: 62%

“…In recent years, many studies have been carried out on the undisturbed measurement of heart rate, but they lack the application in brain fatigue detection. For example, Chen et al designed a BCG monitoring system based on a seven-core fiber interferometer, and the mean difference between the standard monitored heart rate and their proposed sensor's heart rate was 1.19 beats/min [20]. It can be seen that the fiber-optic sensor cushion used in this study can accurately and reliably detect and measure heart rate, which lays a foundation for HRV analysis and provides an accurate, reliable, and practical hardware foundation for quantitative brain fatigue evaluation.…”

Section: Resultsmentioning

confidence: 99%

“…In recent years, sensors have been widely used to detect BCG signals including two kinds of popular sensors, piezoelectric film sensors and optical fiber sensors. Both of them can perform interference-free measurement and obtain human heart rate information without burden [16,20]. ECG signal has been widely used for brain fatigue detection [23][24][25][26], but lacks the application of a BCG signal in brain fatigue evaluation.…”

Section: Descriptions Charactersmentioning

confidence: 99%

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A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram

Yang

et al. 2021

Healthcare

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Brain fatigue is often associated with inattention, mental retardation, prolonged reaction time, decreased work efficiency, increased error rate, and other problems. In addition to the accumulation of fatigue, brain fatigue has become one of the important factors that harm our mental health. Therefore, it is of great significance to explore the practical and accurate brain fatigue detection method, especially for quantitative brain fatigue evaluation. In this study, a biomedical signal of ballistocardiogram (BCG), which does not require direct contact with human body, was collected by optical fiber sensor cushion during the whole process of cognitive tasks for 20 subjects. The heart rate variability (HRV) was calculated based on BCG signal. Machine learning classification model was built based on random forest to quantify and recognize brain fatigue. The results showed that: Firstly, the heart rate obtained from BCG signal was consistent with the result displayed by the medical equipment, and the absolute difference was less than 3 beats/min, and the mean error is 1.30 ± 0.81 beats/min; secondly, the random forest classifier for brain fatigue evaluation based on HRV can effectively identify the state of brain fatigue, with an accuracy rate of 96.54%; finally, the correlation between HRV and the accuracy was analyzed, and the correlation coefficient was as high as 0.98, which indicates that the accuracy can be used as an indicator for quantitative brain fatigue evaluation during the whole task. The results suggested that the brain fatigue quantification evaluation method based on the optical fiber sensor cushion and machine learning can carry out real-time brain fatigue detection on the human brain without disturbance, reduce the risk of human accidents in human–machine interaction systems, and improve mental health among the office and driving personnel.

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Section: Descriptions Characterssupporting

confidence: 62%

Section: Resultsmentioning

confidence: 99%

Section: Descriptions Charactersmentioning

confidence: 99%

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A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram

Yang

et al. 2021

Healthcare

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“…R. Wang et al used an FMF-based interferometer to focus on the analysis of respiration, such as RR, tidal volume (TV) and minute ventilation (MV) [37]. For the MCF interferometer, F. Tan et al employed twin-core fiber (TCF) [46], and W. Chen et al adopted seven-core fiber (SCF) [38]. The structures are shown in Figure 2b,c, respectively.…”

Section: Optical Fiber Mach-zehnder Interferometermentioning

confidence: 99%

“…Due to the high cost of the FBG sensors and the limited sensitivity of the intensity-based sensors, researchers chose the interferometric FOS for vital signs monitoring. Researchers have dedicated much time and effort to the design of sensor structures [12][13][14][15][16][17][18][19][20][21][22][23], demodulation methods [12,[24][25][26][27][28][29][30][31][32][33][34][35][36] and the application of vital signs monitoring [12][13][14]16,[18][19][20][21][22][23][35][36][37][38][39][40][41][42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Vital Signs Monitoring Based on Interferometric Fiber Optic Sensors

et al. 2022

Self Cite

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Due to the improvement of living standards, people’s attention to health has gradually increased. More and more people are willing to spend money and time on health management. This article reviews work on the vital signs monitoring system based on fiber optic interferometers, including the design of sensor structures, signal demodulation methods and data analysis. After a large number of trials, the system can achieve long-term stable heart rate (HR), respiration rate (RR) and body temperature monitoring, and the collected data can be used for health analysis. Due to the high sensitivity, low cost, and light weight of the interferometric fiber optic sensor, it can be integrated under a mattress or a cushion, which is very suitable for daily use. The system has great application prospects in the field of healthcare.

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A deep learning method for contactless emotion recognition from ballistocardiogram

Yu,

Zou,

Mou

et al. 2025

Biomedical Signal Processing and Control

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Non-Invasive Measurement of Vital Signs Based on Seven-Core Fiber Interferometer

Cited by 22 publications

References 26 publications

A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram

A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram

Vital Signs Monitoring Based on Interferometric Fiber Optic Sensors

A deep learning method for contactless emotion recognition from ballistocardiogram

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