Passive invasion detection technology has been widely used in fire rescue

empty nesters care

mine safety and other areas. The traditional invasion detection technology based on wireless sensors identification technology mostly only identify whether someone invades

but some high-risk states of human body such as falling can not effectively identified. To solve this problem

propose a passive invasion detection model based on multistate recognition of human body

build multi-layer signal links to compare each features of data set and use data flow feature extraction method based on sliding window

this model achieves invasion states detection of various human body. Adopt a variety of multi-classification algorithm to simulate and analyze

and further carry out experiment of human body multi-state positioning and tracking. The results show that the detection model has good recognition effect on normal（ nobody）

standing

falling down and lying down states.