## 2 Methods
### Experimental Protocol  
The experiment used a physiological signal measurement system, K&H KL-710, which had an ECG module and a PPG module. The sampling rate was 500 Hz, and the bandwidths of the ECG module and PPG module were tuned 0.5 to 40 Hz and 0.5 to 10 Hz, respectively. The participants in the experiment were recruited through our webpage and written announcements. The purpose and the process of the experiment, as well as the rights of the subjects were listed on the website. Ten male subjects were recruited whose ages were \(23.7\pm 0.67\) years (from 22 to 25 years), weights were \(56.6\pm 3.33\) Kg (from 52 to 62 Kg), and heights were \(170.4\pm 3.77\) cm (from 165 to 175 cm). Frail subjects with arrhythmia and asthma were excluded. This experiment was approved by the Research Ethics Committee of China Medical University and Hospital (No. CRREC-105-072), Taichung city, Taiwan.  
Subjects we required to rest for five minutes before the measurements, and they were asked to fill an informed consent form for the experiment. The subjects' information was recorded, including age, weight, height, medical treatment of illness, and whether there was arrhythmia (if any, excluding from the experiment). An electronic blood pressure monitor (Omron, HM-7210, Japan) was used to measure the subjects' blood pressure. These blood pressures were used as the reference blood pressure in this experiment. The cuff of the blood pressure monitor was wrapped on the right upper arm, the probe of the PPG was placed on the first finger of the left hand, and the electrodes of the ECG were placed on the chest to measure the lead II equivalent signal. The subject code on a fitness bike. The experiment procedure is described below:  
1. The signals in the first five minutes were used as the baseline of the experiment. At the same time, the blood pressure was measured once and its finish-time would be marked at the PPG signal.
2. The pedaling speed was about 80 rpm and was kept continuous for at least five minutes.
3. Then, the blood pressure was measured and its finish-time would be marked at the PPG signal.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Formula** \\ \hline Number of input feature vector (X) & 5 \\ Number of output feature vector (Y) & 1 \\ Number of hidden layers: & 9 \\ Number of the hidden unit on the hidden layers: & \{12, 20, 50, 50, 30, 30, 20, 20, 15\} \\ Learning rate for weight & 0.01 \\ Learning rate for biases of visible units & 0.01 \\ Learning rate for biases of hidden units & 0.01 \\ Momentum rate & 0.9 \\ Maximum epoch in the pre-training: & 10000 \\ Maximum epoch in the fine-training: & 10000 \\ Initial weights and biases: & randomly between [–1,1] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter setting of the deep neural network.  
4 Subjects were requested to rest and to wait for their blood pressure to drop to the baseline. The blood pressures of the subjects were measured repeatedly once every minute within the resting duration, and its finish-time would be marked at the PPG signal.
5 Repeat steps 1 to 5, five times.  
Table 3 shows the statistics, mean and standard deviation (mean \(\pm\) SD), and the maximum and minimum blood pressures of the subjects for the exercise. Three-pulse waves close to the marked point were extracted using manual selection, and the average of these parameters for the three-pulse waves was used to construct the blood pressure model.