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

Healthcare ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1453
Author(s):  
Yanting Xu ◽  
Zhengyuan Yang ◽  
Gang Li ◽  
Jinghong Tian ◽  
Yonghua Jiang
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Heart Rate ◽  
Random Forest ◽  
Optical Fiber ◽  
Optical Fiber Sensor ◽  
Fiber Sensor ◽  
Absolute Difference ◽  
Fatigue Evaluation ◽  
Fatigue Detection

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.

scholarly journals Accurate Estimation of Heart and Respiration Rates Based on an Optical Fiber Sensor Using Adaptive Regulations and Statistical Classifications Spectrum Analysis

2021 ◽  
Vol 3 ◽  
Author(s):  
Rongjian Zhao ◽  
Lidong Du ◽  
Zhan Zhao ◽  
Xianxiang Chen ◽  
Jie Sun ◽  
...  
Keyword(s):  
Heart Rate ◽  
Individual Differences ◽  
Optical Fiber ◽  
Confidence Intervals ◽  
Respiration Rate ◽  
Spectrum Analysis ◽  
Optical Fiber Sensor ◽  
Fiber Sensor ◽  
Respiration Rates ◽  
The Mean

The aim of this work is to present a method for accurately estimating heart and respiration rates under different actual conditions based on a mattress which was integrated with an optical fiber sensor. During the estimation, a ballistocardiogram (BCG) signal, which was obtained from the optical fiber sensor, was used for extracting the heart rate and the respiration rate. However, due to the detrimental effects of the differential detector, self-interference, and variation of installation status of the sensor, the ballistocardiogram (BCG) signal was difficult to detect. In order to resolve the potential concerns of individual differences and body interferences, adaptive regulations and statistical classifications spectrum analysis were used in this paper. Experiments were carried out to quantify heart and respiration rates of healthy volunteers under different breathing and posture conditions. From the experimental results, it could be concluded that (1) the heart rates of 40–150 beats per minute (bpm) and respiration rates of 10–20 breaths per minute (bpm) were measured for individual differences; (2) for the same individuals under four different posture contacts, the mean errors of heart rates were separately 1.60 ± 0.98 bpm, 1.94 ± 0.83 bpm, 1.24 ± 0.59 bpm, and 1.06 ± 0.62 bpm, in contrast, the mean errors of the polar beat device were 1.09 ± 0.96 bpm, 1.44 ± 0.99 bpm, and 1.78 ± 0.94 bpm. Furthermore, the experimental results were validated by conventional counterparts which used skin-contacting electrodes as their measurements. It was reported that the heart rate was 0.26 ± 2.80 bpm in 95% confidence intervals (± 1.96SD) in comparison with Philips sure-signs VM6 medical monitor, and the respiration rate was 0.41 ± 1.49 bpm in 95% confidence intervals (± 1.96SD) in comparison with ECG-derived respiratory (EDR) measurements for respiration rates. It was indicated that the developed system using adaptive regulations and statistical classifications spectrum analysis performed better and could easily be used under complex environments.

scholarly journals Measurement heart rate based on plastic optical fiber sensor

2019 ◽  
Vol 1170 ◽  
pp. 012074
Author(s):  
A Arifin ◽  
A K Lebang ◽  
M Yunus ◽  
S Dewang ◽  
I Idris ◽  
...  
Keyword(s):  
Heart Rate ◽  
Optical Fiber ◽  
Optical Fiber Sensor ◽  
Fiber Sensor ◽  
Plastic Optical Fiber

Method of Measuring Tendon Force using Optical Fiber Sensor

2018 ◽  
Vol 56 (1) ◽  
pp. 94-99
Author(s):  
N. Sogabe ◽  
S. Nakaue ◽  
K. Chikiri ◽  
M. Hayakawa
Keyword(s):  
Optical Fiber ◽  
Optical Fiber Sensor ◽  
Fiber Sensor ◽  
Tendon Force

scholarly journals Oxygen concentration measurement in the porous cathode of a lithium-air battery using a fine optical fiber sensor

2019 ◽  
Vol 5 (0) ◽  
pp. 19-00095-19-00095
Author(s):  
Shogo FUJIMOTO ◽  
Suguru UEMURA ◽  
Nobuyuki IMANISHI ◽  
Shuichiro HIRAI
Keyword(s):  
Optical Fiber ◽  
Oxygen Concentration ◽  
Optical Fiber Sensor ◽  
Concentration Measurement ◽  
Fiber Sensor ◽  
Porous Cathode ◽  
Air Battery

scholarly journals Measurement Improvement of Distributed Optical Fiber Sensor via Lorenz Local Single Peak Fitting Algorithm

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1166
Author(s):  
Bin Liu ◽  
Jianping He ◽  
Shihai Zhang ◽  
Yinping Zhang ◽  
Jianan Yu ◽  
...  
Keyword(s):  
Optical Fiber ◽  
Frequency Shift ◽  
Optical Fiber Sensor ◽  
Gain Spectrum ◽  
Fiber Sensor ◽  
Peak Fitting ◽  
Fitting Algorithm ◽  
Distributed Optical Fiber Sensor ◽  
Brillouin Gain ◽  
Distributed Optical Fiber

Brillouin frequency shift (BFS) of distributed optical fiber sensor is extracted from the Brillouin gain spectrum (BGS), which is often characterized by Lorenz type. However, in the case of complex stress and optical fiber self damage, the BGS will deviate from Lorenz type and be asymmetric, which leads to the extraction error of BFS. In order to enhance the extraction accuracy of BFS, the Lorenz local single peak fitting algorithm was developed to fit the Brillouin gain spectrum curve, which can make the BSG symmetrical with respect to the Brillouin center frequency shift. One temperature test of a fiber-reinforced polymer (FRP) packaged sensor whose BSG curve is asymmetric was conducted to verify the idea. The results show that the local region curve of BSG processed by the developed algorithm has good symmetry, and the temperature measurement accuracy obtained by the developed algorithm is higher than that directly measured by demodulation equipment. Comparison with the reference temperature, the relative measurement error measured by the developed algorithm and BOTDA are within 4% and 8%, respectively.

scholarly journals Design and validation of fiber optic localized surface plasmon resonance sensor for thyroglobulin immunoassay with high sensitivity and rapid detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hyeong-Min Kim ◽  
Dae Hong Jeong ◽  
Ho-Young Lee ◽  
Jae-Hyoung Park ◽  
Seung-Ki Lee
Keyword(s):  
Surface Plasmon Resonance ◽  
Optical Fiber ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
Localized Surface Plasmon Resonance ◽  
Optical Fiber Sensor ◽  
Fiber Sensor ◽  
Localized Surface Plasmon ◽  
Simple Method ◽  
Resonance Sensor

AbstractA simple optical fiber sensor based on localized surface plasmon resonance was constructed for direct and rapid measurement of thyroglobulin (Tg). Specific tests for Tg in patients that have undergone thyroidectomy are limited because of insufficient sensitivity, complicated procedures, and in some cases, a long time to yield a result. A sensitive, fast, and simple method is necessary to relieve the psychological and physical burden of the patient. Various concentrations of Tg were measured in a microfluidic channel using an optical fiber sensor with gold nanoparticles. The sensor chip has a detection limit of 93.11 fg/mL with no specificity for other antigens. The potential applicability of the Tg sensing system was evaluated using arbitrary samples containing specific concentrations of Tg. Finally, the sensor can be employed to detect Tg in the patient’s serum, with a good correlation when compared with the commercial kit.

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