## 1 Introduction  
The last decade has seen the development of some wearable technologies for health care [1]. These technologies are used for monitoring the saturation of percutaneous oxygen (SpO\({}_{2}\)) [2], the electrocardiogram (ECG) [3], body temperature, physical activities [4], and the respiratory rate. These aforementioned measurement techniques can be combined into a single device that performs a complete physiological monitoring, similar to physiological clothes and polysomnography. However, the devices do not collect blood pressure measurements because a cuff-type blood pressure measurement requires mechanical devices like an air-pump motor and an electromagnetic valve. Most commercial automatic blood-pressure monitors apply either auscultatory or oscillometric methods [5], and these cannot be easily built into wearable devices. [PERSON] et al. proposed a novel technique for a cuffless blood pressure measurement, where they used the pulse transit time (PTT) to evaluate the systolic blood pressure [6]. They found that this method could monitor the change in a person's blood pressure pattern. [PERSON] et al. used the PTT to evaluate the change in blood pressure during obstetric spinal anesthesia [7]. The PTT is defined as the time taken by the pulse wave to travel from the systolic ending of the left ventricular to the specific position of the peripheral artery. Therefore, the cuffless technique must include at least two sensors, that is, one placed on the chest and another placed on the arm or the leg. Both studies used the ECG and photoplethysmogram (PPG) to obtain the PTT [6,7]. However, the PTT has a stronger relation with [PERSON]'s modulus of the artery. Blood pressure is one parameter of the cardiac hemodynamic theorem and, therefore, it is difficult to accurately estimate the blood pressure using only the PTT parameter. Cardiac hemodynamic parameters include blood pressure, cardiac output, and systemic vascular resistance. In the Windkessel model [8], the pulse contour method was used to estimate cardiac output, and it can be used to estimate arterial stiffness using the Augmentation Index [9]. [PERSON] et al. used the PTT and characteristics of the PPG wave to estimate the continuous blood pressure using a neural network (NN) [10]. [PERSON] et al. used parameters extracted from the ECG and blood pressure waveform to estimate the blood pressure using a multilayer perceptron neural network [11]. [PERSON] et al. used some of the parameters extracted from the PPG wave to estimate blood pressure [12]. In these studies, the three-layer NN with back propagation algorithm was linked to the regression model. The deep neural network (DNN) has also been utilized to perform a regression model for blood pressure measurements [13]. However, all these studies focused on the static blood pressure measurement and they required significant data to estimate the blood pressure. If the cuffless blood pressure measurement were to be applied to a wearable device, the blood pressure measurement should be under a dynamic condition, and only a few data points would be used to build the model. Thus, the cuffless blood pressure measurement not only uses the PPT parameter, but it also has to combine some contour characteristics of pulse. The pulse wave can be measured using a pressure sensor [14], tonometer [15], and PPG, where PPG is commonly used to measure the pulse wave, representing the plethysmogram of the arteries. However, the pulse wave measured by PPG is easily affected by some factors like the subject's skin, tissue, and light density of the LED. Moreover, it is hard to calibrate the device. PTT measured using PPG and an ECG also depends on the placement of the PPG sensor [16]. Consequently, the goal of this study was to explore cuffless blood pressure measurements using the contour characteristics of the pulse measured by PPG. Ten subjects participated in this study and they were asked to exercise to raise their blood pressures.