###### Abstract  
Pulse wave signal sensed over the radial artery on the wrist is a crucial physiological indicator in disease diagnosis. The sensor array composed of multiple sensors has the ability to collect abundant pulse wave information. As a result, it has gradually attracted the attention of practitioners. However, few practical methods are used to obtain a one-dimensional pulse wave from the sensor array's spatial multi-dimensional signals. The current algorithm using pulse wave with the highest amplitude value as the significant data suffers from low consistency because the signal acquired each time differs significantly due to the sensor's relative position shift to the test area. This paper proposes a processing method based on time series similarity, which can take full advantage of sensor arrays' spatial multi-dimensional characteristics and effectively avoid the above factors' influence. A pulse wave acquisition system (PWAS) containing a micro-electro-mechanical system (MEMS) sensor array is continuously extruded using a stable dynamic pressure input source to simulate the pulse wave acquisition process. Experiments are conducted at multiple test locations with multiple data acquisitions to evaluate the performance of the algorithm. The experimental results show that the newly proposed processing method using time series similarity as the criterion has better consistency and stability. micro-electro-mechanical system (MEMS); flexible sensor array; time series similarity; Dynamic Time Warping (DTW); pulse signal +
Footnote †: journal:  
article  
[color=black][color=blackHowever, this diagnostic method that relies on long-term training and rich experience has specific subjective errors. Therefore, a digital acquisition system capable of objectively measuring pulse wave signals has been proposed and studied. Various types of sensors, including piezoelectric [9; 10; 11], piezoresistive [12; 13; 14], photoelectric [15; 16; 17], and ultrasonic [18; 19] sensors, have been developed and improved to capture weak pulse wave signals. With advances in sensors, manufacturing technology, and signal processing methods, a wide variety of pulse wave acquisition systems have emerged [20; 21; 22; 23; 24]. The equipment used to assist TCM diagnosis has expanded from a single-probe type [25] to a three-probe type [22; 26] that can collect three positions of Cun, Guan, and Chi at the same time, and even reaches a five-probe type [21]. The signal acquisition for a single test location is also updated from a single sensor to a sensor array composed of multiple sensors [24; 27]. By comparing and analyzing the difference between the pulse wave waveform collected by a single sensor and the standard pulse waveform, it can assist the doctor in the diagnosis. However, it is impossible to obtain spatially relevant information, such as the pulse wave's length and width [8; 25]. The acquisition system, including a sensor array, can acquire multi-dimensional pulse wave signals in space and then calculate the pulse wave's length and width characteristics to compensate for the defects, as mentioned above [23; 24; 27]. However, there are few studies on how to obtain a single valid and stable pulse waveform from multiple sensors [28; 29]. Some researchers take the sensor with the highest amplitude value of the collected signal in a single cycle as the representative sensor. Moreover, the signal collected by this sensor is the effective pulse waveform of the whole sensor array [30]. Other researchers interpolate the spatial multi-dimensional signals collected by the sensor array to obtain a 3D pulse waveform signal map. The highest point in each three-dimensional pulse wave signal graph in one acquisition cycle is extracted as the pulse wave signal of the entire cycle [24; 27]. The dynamic force applied to different sensors changes during multiple acquisitions due to slight differences in the test positions covered by the sensor arrays.