In recent years, Traditional Chinese Medicine (TCM) has attracted more and more attention due to its good therapeutic effect, low cost, and convenience. This research is also a part of the goal of the modernization of TCM. Based on the meridian electric potential acquisition system independently developed by our project team, in this paper, we designed the human body's meridian electric potential acquisition scheme. We use principal component analysis (PCA) to prove that the meridional potential signal is derived from the ECG signal. Then, Inception ResNet V2 was used to classify acupoints and nonacupoints. Finally, the classification accuracy rate reached 86.59045265, and the F1 score = 0.72161642. This shows that acupoints and nonacupoints can be distinguished by their surface potential.

As traditional Chinese medicine (TCM) has gained more and more recognition in the world, Chinese medicine has also played its important role. However, traditional Chinese medicine equipment is relatively deficient, with insufficient functions and low degree of digitalization. For example, existing auscultation equipment can obtain few human characteristic indicators, which is difficult to meet the needs of reference in traditional Chinese medicine diagnosis. Based on this, this paper designed a human body characteristic index detection system based on the principle of traditional Chinese medicine, which includes respiratory and heartbeat signal acquisition device, meridian and acupoint signal acquisition device, temperature signal acquisition device, pulse and blood pressure acquisition device, processing module, keyword module, and output module. The respiratory and heartbeat signal acquisition device is used to collect the respiratory and heartbeat signal of human body. Meridian acupoint signal acquisition device is used to collect human meridian acupoint radio signals. The temperature signal acquisition device is used to collect the infrared temperature light wave signal of human body. Pulse and blood pressure acquisition devices are used to collect pulse and blood pressure signals. The processing module is used to obtain one or more human body characteristic indicators according to one or more of the respiration and heartbeat signals, meridians and acupoints signals, temperature signals, pulse, and blood pressure, including Qi and blood characteristic indicators, viscera and six meridian characteristic indicators, and temperature characteristic indicators. The keyword corresponding module is used to obtain the corresponding keyword representing the physiological state information of human body according to the one or more human body characteristic indicators. The output module is used to output the human body characteristic index and the key words. It includes the key words of Qi and blood state information, the key words of viscera state information, the key words of Qi and blood state information, etc. The system can be used for serious disease screening, chronic disease management, and risk early warning.

In this paper, an FPGA-based convolutional neural network coprocessor is proposed. The coprocessor has a 1D convolutional computation unit PE in row stationary (RS) streaming mode and a 3D convolutional computation unit PE chain in pulsating array structure. The coprocessor can flexibly control the number of PE array openings according to the number of output channels of the convolutional layer. In this paper, we design a storage system with multilevel cache, and the global cache uses multiple broadcasts to distribute data to local caches and propose an image segmentation method that is compatible with the hardware architecture. The proposed coprocessor implements the convolutional and pooling layers of the VGG16 neural network model, in which the activation value, weight value, and bias value are quantized using 16-bit fixed-point quantization, with a peak computational performance of 316.0 GOP/s and an average computational performance of 62.54 GOP/s at a clock frequency of 200 MHz and a power consumption of about 9.25 W.