**Acknowledgements**

This work was supported by JSPS KAKNHI Grant Number JP16H01805 and the Wearable vital signs measurement system development project at Shinshu University. This research is (partially) supported by the Creation of a development platform for

*Fiber Optic Sensing - Principle, Measurement and Applications*

of the calculation accuracy was 2.9 mmHg (~2.6%). Similarly, in the diastolic blood pressure, the average value of the verification data for blood pressure calculation was 60.5 mmHg, while the average value of the calculation accuracy was 2.8 mmHg (~4.7%). These results indicate that the calculation accuracy of diastolic blood pressure is lower than that of systolic blood pressure. This is due to step 6 of signal processing, whereby the canceling of the pulse rate fluctuation was performed in order to calculate the FBG sensor signal by the PLS regression analysis. The so-called latter half portion of a single beat of the FBG sensor signal is truncated. There is a peak of expansion initial positive wave in this part, which represents the diastole of the heart. Therefore, it is considered that the deletion of the diastolic information from the FBG sensor signal caused a decline in the calculation accuracy. In other words, in this calculation method, since the negative characteristics of the signal processing is reflected in the result, the blood pressure is calculated from the movement of the heart included in the FBG sensor signal. However, considering that the results of all blood pressure calculations were ±5 mmHg, it is considered that the blood pressure was calculated with high calculation accuracy. Therefore, it is established that the blood pressure can be calculated with high calculation accuracy by constructing the calibration curve by PLS regression analysis of the waveform of the FBG sensor signal. It is evident from this result that it is possible to calculate blood pressure from the same FBG sensor signal in addition to pulse rate, respiratory rate, and stress load.

Calculating accuracy (mmHg) Systolic 3.4 2.8 2.6 2.9

**Subject A B C Average**

Diastolic 4.1 1.8 2.6 2.8

In this paper, the method of calculating each vital sign from the FBG sensor signal was described. An experiment using a biological model demonstrated that the FBG sensor signal was influenced by the change in diameter of the tube through which a fluid (pseudo blood) was allowed to flow by a simulated pressure. This model was replaced with a living body, and a change in the diameter of the artery caused by a change in the flow rate of blood related to the movement of the heart was measured. The FBG sensor signal was measured at a high temporal resolution of 10 kHz; therefore, the pulse rate could be calculated with a high measurement accuracy. Based on the phenomenon of respiratory sinus arrhythmia, the respiration rate could be calculated from the cycle of pulse rate change during expiration and inspiration. On the other hand, the first derivative waveform of the FBG sensor signal was found to be similar to the acceleration pulse wave, which was the second derivative of the volume pulse wave signal; therefore, it was considered that the systolic and diastolic information of the heart was present in the signal. Subsequent to the signal processing of this primary derivative waveform of FBG sensor signal, a calibration curve was constructed by PLS regression analysis for calculating the blood pressure, and accordingly, the blood pressures (systolic and diastolic) were calculated with high accuracy. All these vital signs were calculated from a measured FBG sensor signal. Therefore, considering that only the analysis method is different, it is possible to calculate a plurality of vital signs simultaneously from one measurement signal. Since the FBG sensor signal is continuously measured, the vital signs

**54**

**7. Conclusion**

**Table 4.**

*Results of calculating blood pressure.*

implantable/wearable medical devices by a novel physiological data integration system of the Program on Open Innovation Platform with Enterprises, Research Institute and Academia (OPERA) from the Japan Science and Technology Agency (JST).
