1：Experimental Design and Method Selection:

The study uses FBG sensors to measure pulse waves at two body points (wrist and elbow) and calculates systolic blood pressure based on Pulse Transit Time (PTT). The rationale is to leverage the high sensitivity and non-invasive nature of FBG sensors for continuous vital sign monitoring. Theoretical models include the relationship between PTT and systolic blood pressure, derived from principles like Moens-Korteweg equation.

2：Sample Selection and Data Sources:

Four healthy male subjects in their 20s (Subjects A, B, C, D) were selected. Their height, weight, and age averages and standard deviations are provided in Table 1. Data were acquired from pulse waves measured at the wrist and elbow.

3：Data were acquired from pulse waves measured at the wrist and elbow. List of Experimental Equipment and Materials:

3. List of Experimental Equipment and Materials: Equipment includes a heterodyne FBG sensor system (PF25-S01 by Nagano Keiki Co.), electronic sphygmomanometer (HEM-1020 by OMRON Corp.), medical tape for sensor fixation, and a PC with LabVIEW software (by National Instruments Corp.) for data analysis. Materials involve FBG sensors with specific reflection wavelength ranges (e.g., 1530 ± 0.5 nm, 1550 ± 0.5 nm, 1560 ± 0.5 nm).

4：5 nm, 1550 ± 5 nm, 1560 ± 5 nm). Experimental Procedures and Operational Workflow:

4. Experimental Procedures and Operational Workflow: Sensors were fixed perpendicular to the artery direction on the skin surface at the wrist and elbow using medical tape. Pulse waves were acquired simultaneously with blood pressure measurements using the electronic sphygmomanometer. Measurements were conducted in a supine position to ensure heart-level alignment. Data were sampled at 1 kHz after averaging 20 points from a 20 kHz sampling rate to reduce noise. A bandpass filter (0.5 < f < 5 Hz) was applied to the pulse waves for feature extraction and noise reduction. Peak detection was performed using quadratic polynomial approximation, and PTT was calculated as the average time difference between corresponding peaks from wrist and elbow waveforms over the measurement period.

5：5 < f < 5 Hz) was applied to the pulse waves for feature extraction and noise reduction. Peak detection was performed using quadratic polynomial approximation, and PTT was calculated as the average time difference between corresponding peaks from wrist and elbow waveforms over the measurement period. Data Analysis Methods:

5. Data Analysis Methods: Data analysis involved calculating PTT from filtered pulse waves, constructing calibration curves for systolic blood pressure using leave-one-out cross-validation, and comparing estimated values with reference values from the electronic sphygmomanometer. Statistical analysis included correlation coefficients and standard errors, with a target accuracy of ±5 mmHg. LabVIEW software was used for these analyses.