## 3 Method
Finally, to match the pseudo feature vector for the pseudo subject, we perform the Algorithm 1 as follows:  
```
while\(j\leq J\)do \(S_{j}\leftarrow\text{MGD}(\{H_{s},W_{s},A_{s},G_{s},B_{s}\})\) # \(S_{j}\) is pseudo subject while\(m\leq 8\)do \(F\leftarrow\text{MGD}(\{H_{f},W_{f},A_{f},G_{f},B_{f},P_{f}\})\) # \(F\) is \(100\times(5+P)\) matrix (\(F\leftarrow\{F_{1},F_{2},...,F_{100}\}\) while\(i\leq 100\)do if\(F_{i}\{H_{f},W_{f},A_{f},G_{f},B_{f}\}\) is not matched to condition 1, 2, 3 with \(S_{j}\)then  Delete \(F_{i}\) endif \(i\gets i+1\) endwhile if# of candidate vectors \(>0\)then \(F_{j,m}\leftarrow\min(S_{j}-F)\) else  Continue endif \(m\gets m+1\) endwhile \(j\gets j+1\) endwhile
```  
**Algorithm 1** Matching the pseudo subject with the pseudo feature  
Condition 1 \(|H_{s}-H_{f}|<T_{H},|W_{s}-W_{f}|<T_{W},|A_{s}-A_{f}|<T_{A}\)  
Condition 2 \(G_{s}=G_{f}\) Condition 3 \(|B_{s}-B_{f}|<T_{B}\)  
where \(S_{j}\) denotes a vector for the pseudo subject, which consist of the BP and body information, and \(F\) is the matrix for the candidate pseudo feature, related with specific conditions in Equation (9). And, \(H\), \(W\), \(A\), \(G\), and \(B\) are respectively height, weight, age, gender, and BP. Also, \(j\) and \(J\) denote the index of the pseudo subject and entire pseudo subject, respectively. In addition, \(m\) denotes the index of the pseudo data \(F_{j,m}\) for the pseudo subject, and then only body information from \(F_{j,m}\) is replaced by the body information from \(S_{j}\). \(P\) is dimension of signal-based features. Finally, \(T_{H}\), \(T_{W}\), \(T_{A}\), and \(T_{B}\) denote thresholds of height (cm), weight (kg), age, and BP (mmHg), respectively. When training the NN model, the pseudo data is combined with the real data. Finally, as shown in Figure 3, after the training DB is augmented by our proposed algorithm, we train the NN model for estimating the SBP and DBP using the method described in the Section 2. In addition, after the feature extraction is performed on the smart wristwatches, the extracted feature vector is transmitted to the smartphone connected via Bluetooth to estimate the SBP and DBP based on the NN parameters at the smartphone. In order to verify the DA-MGD algorithm compared with the bootstrap algorithm, we trained the BPE model based on the NN with the augmented training DB. For this, the training DB was augmented as 5 times and 20 times using the bootstrap algorithm, and we then trained the NN model. And, to augment the training DB using the proposed algorithm, the pseudo subjects were created additionally with 50 and 100 subjects. And, \(T_{H}\), \(T_{W}\), \(T_{A}\), and \(T_{B}\) were set to 5, 5, 5, and 10, respectively. Since the loop in a code of the algorithm runs infinitely if the thresholds that are too small was used, we empirically set it to 5 to make the algorithm work smoothly. In addition, the threshold of the BP was set to 10 because the difference of 10 mmHg between the trials of the actual BP could occur. We compared the NN models using the data augmentation algorithms with the baseline NN model without the data augmentation. To train the NN models, the number of hidden layers was set to three and the number of hidden units on each layer was set to 128, 256, and 128, respectively. We used the same learning parameters for all experiments to evaluate the performance of the data augmentation algorithms. Also, the number of hidden layers and units was determined empirically through experiments with the best performance. To alleviate the overfitting problem, we employed the drop-out (0.2) and L1 regularization.