摘要：With rapid population aging and increasingly indoor sensing technologies, mining effective information in sensor data is in need that we can analyse individual behaviour semantics, or even predict intentions. The model for indoor activity recognition (AR) is usually based on statistic while sensor data can impliedly reflect abundant information in order. Behaviour will trigger environment perception sensors. Inspired by information transmission in nature, persistent action keeps activity pheromone accumulating and inactive action keeps it volatilizing along with time shift. Different from statistic model, our framework proposes a method to construct multi factor features named activity pheromone matrix (APM). It has a double-layer model for recognizing daily activities include the high-overlapping. The experimental results show that our method can effectively promote the accuracy of activities recognition compared with the existing statistical models, even the high-overlapping activities in small areas.
