During pulse signal collection, width information of pulse waves is essential for the diagnosis of disease. However, currently used measuring instruments can only detect the amplitude while can’t acquire the width information. This paper proposed a novel wrist pulse signal acquisition system, which could realize simultaneous measurements of the width and amplitude of dynamic pulse waves under different static forces. A tailor-packaged micro-electro-mechanical system (MEMS) sensor array was employed to collect pulse signals, a conditioning circuit was designed to process the signals, and a customized algorithm was developed to compute the width. Experiments were carried out to validate the accuracy of the sensor array and system effectiveness. The results showed the system could acquire not only the amplitude of pulse wave but also the width of it. The system provided more information about pulse waves, which could help doctors make the diagnosis.

In pulse waveform classification, the convolution neural network (CNN) shows excellent performance. However, due to its numerous parameters and intensive computation, it is challenging to deploy a CNN model to low-power devices. To solve this problem, we implement a CNN accelerator based on a field-programmable gate array (FPGA), which can accurately and quickly infer the waveform category. By designing the structure of CNN, we significantly reduce its parameters on the premise of high accuracy. Then the CNN is realized on FPGA and optimized by a variety of memory access optimization methods. Experimental results show that our customized CNN has high accuracy and fewer parameters, and the accelerator costs only 0.714 W under a working frequency of 100 MHz, which proves that our proposed solution is feasible. Furthermore, the accelerator classifies the pulse waveform in real time, which could help doctors make the diagnosis.

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.