In order to achieve the goal of low power consumption and low latency for edge-end human activity recognition,this paper designs a fast recognition system based on wearable sensors and Convolutional Neural Networks (CNNs).First,the system collects data through sensors to make a human activity recognition dataset,and pre-trains a CNN-based behavior recognition model on the PC side,which achieves an accuracy of 93.61% on the test set.Then,hardware acceleration is realized through methods such as data fixed point,convolution kernel multiplexing,parallel processing of data,and pipeline.Finally,the recognition model is deployed on the FPGA,and the collected sensor data are input into the system to realize the recognition of human activity at the edge.The whole system is developed jointly with hardware and software based on Ultra96-V2.The experimental results show that when the input clock is 200 M,the system runs on FPGA with an accuracy of 91.80%;the proposed system is superior to CPU in recognition speed as well as power consumption,specifically,the power consumption is only one-tenth of CPU consumed,and energy consumption ratio is 91% higher than that of GPU.It can be concluded that the FPGA-based human activity recognition system meets the design requirements of low power consumption and low delay.