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摘要
针对目前颈部脉搏监测装置不便携带、信号处理复杂等缺点，设计一款光纤布拉格光栅(fiber Bragg grating, FBG)颈部脉搏监测装置。使用该装置监测志愿者1、2在静坐和仰卧时3种状态(静息、运动和剧烈运动)下的颈部脉搏各10 s。利用傅里叶变换对其进行处理，得到其频率与手环、血氧仪监测的频率误差都小于10%，对不同状态的各自周期进行皮尔逊相关性分析，得到其相关性大于0.9。利用随机森林对其进行预测分析，得到其预测分析效果较好。分析结果表明，该颈部脉搏监测装置能够有效地监测人体颈部脉搏。
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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Overview
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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图 2 颈部脉搏监测装置。(a)示意图；(b)桥型应变放大结构；(c)受力分析
Figure 2. Neck pulse monitoring device. (a) Schematic; (b) Bridge strain amplification structure; (c) Force analysis
图 4 颈部监测装置仿真。(a)传感单元；(b)传力结构
Figure 4. Simulation results of neck monitoring device. (a) Sensing unit; (b) Force transfer structure
图 5 颈部监测装置力标定。(a)标定试验台；(b)传感单元标定；(c)标定实验数据分析
Figure 5. Force calibration of neck monitoring device. (a) Calibration test bench; (b) Calibration of sensing unit; (c) Calibration experimental data analysis
图 6 温度对照实验。(a)实验平台；(b)实验数据
Figure 6. Temperature control experiment. (a) Experimental platform; (b) Experimental data
图 7 老化实验。(a)温度老化数据；(b)力老化数据
Figure 7. Aging test. (a) Temperature aging data. (b) Force aging data
图 10 监测数据。(a)志愿者1静坐；(b) 志愿者1仰卧；(c) 志愿者2静坐；(d)志愿者2仰卧
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
图 11 静息、运动、剧烈运动状态的第一个完整周期。(a)志愿者1静坐；(b) 志愿者1仰卧；(c) 志愿者2静坐；(d)志愿者2仰卧
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
图 12 静息、运动、剧烈运动状态的频率对比。(a)志愿者1静坐；(b) 志愿者1仰卧；(c) 志愿者2 静坐；(d)志愿者2仰卧
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
图 13 静息、运动、剧烈运动状态各自周期的相关性分析。(a)志愿者1静坐；(b) 志愿者1仰卧；(c) 志愿者2 静坐；(d)志愿者2仰卧
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
图 14 静息、运动、剧烈运动状态的随机森林误差与决策树数目分析。 (a)志愿者1静坐；(b) 志愿者1仰卧；(c) 志愿者2静坐；(d)志愿者2仰卧
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
表 1 监测装置-手环-血氧仪脉搏监测对比数据
Table 1. Monitoring device-bracelet - oximeter pulse monitoring comparison data
Volunteer Posture State Frequency/(times/min) Ws/% Wx/% Monitoring device Wristband Oximeter Volunteer 1 Sit Resting 66 73 72 9.58 6.69 Exercise 96 99 91 3.03 5.50 Vigorous exercise 120 119 117 0.83 2.56 Supine Resting 69 71 72 2.82 4.17 Exercise 96 93 91 3.23 5.50 Vigorous exercise 126 118 122 6.78 3.28 Volunteer 2 Sit Resting 72 74 75 2.70 4.00 Exercise 99 91 92 8.79 7.61 Vigorous exercise 126 121 118 4.13 6.78 Supine Resting 72 70 76 2.86 5.26 Exercise 102 94 94 8.51 8.51 Vigorous exercise 129 123 121 4.88 6.61 
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表 2 静息、运动、剧烈运动状态的评价指标
Table 2. Evaluation indicators of resting, exercise, and vigorous exercise states
Posture Dataset Index Resting Exercise Vigorous exercise Volume 1 sit Training set MAE 0.4669 0.4631 0.4900 MBE -0.0021 0.0004 0.0033 RMSE 0.4589 0.4325 0.4711 Prediction set MAE 0.4671 0.4694 0.4993 MBE 0.0012 0.0009 0.0026 RMSE 0.4626 0.4256 0.4692 Volume 1 lies in supine position Training set MAE 0.4787 0.4497 0.4735 MBE −0.0015 0.0041 0.0002 RMSE 0.4871 0.4806 0.4838 Prediction set MAE 0.4878 0.4478 0.4809 MBE −0.0022 −0.0011 −0.0013 RMSE 0.4817 0.4803 0.4823 Volume 2 sit Training set MAE 0.4866 0.4742 0.4844 MBE −0.0031 −0.0016 −0.0033 RMSE 0.4789 0.4854 0.4857 Prediction set MAE 0.4927 0.4616 0.4856 MBE 0.0011 −0.0348 0.0015 RMSE 0.4819 0.4777 0.4801 Volume 2 lies in supine position Training set MAE 0.4712 0.4847 0.4685 MBE 0.0038 −0.0016 0.0012 RMSE 0.4815 0.4876 0.4883 Prediction set MAE 0.4739 0.4804 0.4692 MBE 0.0012 0.0011 −0.0019 RMSE 0.4905 0.4916 0.4811
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