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Yan X Y, Feng Y, Zhang H. Analysis method of fiber grating neck pulse monitoring device[J]. Opto-Electron Eng, 2025, 52(4): 250022. doi: 10.12086/oee.2025.250022
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Analysis method of fiber grating neck pulse monitoring device
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Abstract
In response to the current limitations of neck pulse monitoring devices, such as being inconvenient to carry and having complex signal processing, a fiber Bragg grating (FBG) based neck pulse monitoring device was designed. The device monitored two volunteers in three states (resting, exercise, and vigorous exercise) while sitting and lying down for 10 s each. Fourier transform was applied to process the data, and the frequency error between the neck pulse device, the wristband, and the pulse oximeter was found to be less than 10%. Pearson correlation analysis was conducted on the periods of different states, with the correlation coefficient exceeding 0.9. Random forest was used for predictive analysis, and the results showed good prediction performance. The analysis indicates that the neck pulse monitoring device is capable of effectively monitoring the pulse in the neck region of the human body. -
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References
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Overview
A wearable fiber Bragg grating neck pulse monitoring device has been designed to address the shortcomings of current neck pulse monitoring devices, including inconvenience in wearing and complex signal processing. This device is not affected by temperature, offers portability and comfort, enhances monitoring sensitivity, and can track the neck pulse frequency of the human body under different states. The device has been optimized for comfort, ensuring that users experience greater comfort during monitoring. Calibration experiments have shown that its pressure sensitivity is 40 pm/N, with a fitting goodness of 0.9985. The error between the theoretical sensitivity and the calibration experimental sensitivity is only 2.663%, which is relatively low. A temperature comparison experiment was conducted, and the maximum error was found to be 1.750%, demonstrating that the performance of the device is minimally affected within a certain temperature range. The device underwent 72 h aging experiments under temperature and force conditions, and the maximum wavelength variation at adjacent time points was 1.12 pm and 1.11 pm, indicating minimal change and proving that its performance is not significantly affected under these conditions. The device was subjected to a 1 h signal attenuation experiment, where the maximum attenuation rate was less than 0.1%, indicating that the signal attenuation over the hour was negligible. volunteer 1 used the device to monitor the neck pulse for 10 s at 15:00, 17:00, 19:00, 21:00, and 23:00 on the same day. The ICC coefficient of the five monitoring data points was 0.99383, indicating high consistency between the five sets of data. The device was used to monitor volunteers 1 and 2 under sitting and lying down in different states (resting, exercise, and vigorous exercise) for 10 s each, and it was observed that while the peaks and valleys of the pulse waves exhibited some differences, their periodicity was almost consistent. The first complete cycle of each state was processed and analyzed by spline interpolation, and the results of comparison with theoretical pulse wavelength changes showed consistent trends. Fourier transform processing was applied to the data, and the frequency error with that of wristbands and pulse oximeters was found to be less than 10%. Pearson correlation coefficient of the periods for different states yielded a correlation greater than 0.9. Finally, random forest was used for predictive analysis, and the evaluation results showed that the prediction was accurate. The analysis above indicates that the neck pulse monitoring device can effectively monitor the neck pulse of the human body.
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Yan X Y, Feng Y, Zhang H. Analysis method of fiber grating neck pulse monitoring device[J]. Opto-Electron Eng, 2025, 52(4): 250022. doi: 10.12086/oee.2025.250022
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Figure 1.
Working principle diagram of the fiber grating sensor
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Figure 2.
Neck pulse monitoring device. (a) Schematic; (b) Bridge strain amplification structure; (c) Force analysis
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Figure 3.
Pulse wave signal feature division
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Figure 4.
Simulation results of neck monitoring device. (a) Sensing unit; (b) Force transfer structure
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Figure 5.
Force calibration of neck monitoring device. (a) Calibration test bench; (b) Calibration of sensing unit; (c) Calibration experimental data analysis
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Figure 6.
Temperature control experiment. (a) Experimental platform; (b) Experimental data
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Figure 7.
Aging test. (a) Temperature aging data. (b) Force aging data
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Figure 8.
Experimental data of signal attenuation
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Figure 9.
Experimental platform of neck pulse monitoring device
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Figure 10.
Monitoring data. (a) Volunteer 1 sit; (b) Volunteer 1 lies in supine position; (c) Volunteer 2 sit; (d) Volunteer 2 lies in supine position
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Figure 11.
First full cycle of resting, exercise, and vigorous exercise state. (a) Volunteer 1 sit; (b) Volunteer 1 lies in supine position; (c) Volunteer 2 sit; (d) Volunteer 2 lies in supine position
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Figure 12.
Frequency comparison of resting, exercise, and vigorous exercise states. (a) Volunteer 1 sit; (b) Volunteer 1 lies in supine position; (c) Volunteer 2 sit; (d) Volunteer 2 lies in supine position
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Figure 13.
Correlation analysis of resting, exercise, and vigorous exercise state in each cycle. (a) Volunteer 1 sit; (b) Volunteer 1 lies in supine position; (c) Volunteer 2 sit; (d) Volunteer 2 lies in supine position
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Figure 14.
Random forest error and decision tree number analysis of resting, exercise and vigorous exercise states. (a) Volunteer 1 sit;(b) Volunteer 1 lies in supine position; (c) Volunteer 2 sit; (d) Volunteer 2 lies in supine position
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