## 4 Discussion  
Cuffless blood pressure measurement using PTT has been studied by many researchers [6; 7; 10]. The pulse wave can be measured by the PPG [7; 10], impedance plethysmography [17], and a pressure sensor [14]. We found that the accuracy of the measured blood pressure was affected by the measuring conditions. Most of the previous studies have measured blood pressure under a static condition, which would have a high accuracy. However, when blood pressure changes under spinal anesthesia [7] or exercise [17], the accuracy of blood pressure measurements using PTT would become fairly poor. In the pilot study by [PERSON], the correlation coefficient between the mean blood pressure and PTT was only \(r^{2}=0.55\). The result for the exercise experiment was only \(r^{2}=0.49\). Therefore, we used the contour characteristics of the pulse wave to increase the accuracy of the blood pressure measurement when blood pressure has its quickest and continuous change. Figure 5: The measured and estimated blood pressures during five exercises for the fifth subject. (**a**) the first time experiment, (**b**) the second time experiment, (**c**) the third time experiment, (**d**) the fourth time experiment, (**e**) the fifth time experiment. Some studies have also used the parameters extracted from a PPG and ECG to estimate blood pressure using a multilayer perceptron neural network [10; 11], where the results showed good accuracy. However, their studies all focused on the static measurement condition, and there were too many parameters used to estimate blood pressure. In our study, we used a DNN and linear multi-dimension regression method to build the blood pressure model, which only used five parameters. The results showed that a linear multi-dimension regression model has a better accuracy than a DNN. The \(E_{RMS}\) statistics were 6.9 \(\pm\) 2.81 mmHg and 9.5 \(\pm\) 1.71 mmHg for the systolic pressure, respectively, and 4.0 \(\pm\) 0.65 mmHg and 4.7 \(\pm\) 0.58 mmHg for the diastolic pressure, respectively. In this study, only a small volume of data was required for each subject. However, the DNN required a large volume of data. Since we tried to increase the accuracy of the DNN method, the structure complexity of the DNN was increasing to [12; 20; 50; 50; 30; 30; 20; 20; 15]. This presents a bigger problem to utilize the DNN in a wearable device for measuring blood pressure. The linear multi-dimension regression model only had one layer and it could use the pseudoinverse to estimate the multi-dimension regression model, which would save training time and be easily implemented in a wearable device. Although [PERSON] et al. used a NN with two hidden layers to estimate the blood pressure and achieved a good accuracy, the Bland-Altman plots of blood pressure showed that many estimated values were still found outside the limits of the agreement [10]. The results from the calibration-free method showed that there was only 76.9% and 86.4% accuracy in the systolic and diastolic pressures within a 5 mmHg error. According to the BS EN 1060-4 standard [18], the mean error of blood pressure has to be lower than \(\pm\)5 mmHg. Therefore, if there is no method fitting this standard, a simple model would be the solution, which could be easily realized in a wearable device. Another problem in this study was the synchronized blood pressure measurement. We only used an electric blood pressure monitor to measure the blood pressure at different time points intermittently. However, we know that this commercial blood pressure monitor only measures the rough average blood pressure within its measuring duration. Meanwhile, the blood pressure was continuously dropping after exercise. Therefore, the chosen pulses only represented these time points, which do not exactly represent the pulses of the blood pressure measured by the commercial blood pressure monitor.