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To preserve individual privacy and to avoid the effects of light condition and obstacles, this paper designed a human activities recognition system based on binary ceiling infrared sensor network. This system uses STM32 and ZigBee coordinator to build a ZigBee network. When a target does some activities under the interconnection of twenty AMN31111 infrared sensors, binary values will be collected by sensors. Then ZigBee coordinator will send this data to PC real time dynamics through serial port and these values will be stored in txt format by the software of Keil 5. We proposed the method of pixel value to estimate the locations of the target. In a home environment, we used BP neural network algorithm to recognize 7 different activities of a target. The experimental results showed that this system achieved a performance of a single target’s activities recognition rate of 84.7%. The average recognition rate obtained by 4 targets was about 4.7% higher than those sensor devices attached to home appliances. The system is with high precision, stable performance, high reliability, low cost and low power consumption. So it solves some current existing problems in human behavior recognition system.