Detecting Human Activity Profiles with Dirichlet Enhanced Inhomogeneous Poisson Processes

This paper describes an activity pattern mining method via inhomogeneous Poisson point processes (IPPPs) from time-series of count data generated in behavior detection by pyroelectric sensors. IPPP reflects the idea that typical human activity is rhythmic and periodic. We also focus on the idea that activity patterns are affected by exogenous phenomena, such as the day of the week, and weather condition. Because single IPPP could not tackle this idea, Dirichlet process mixtures (DPM) are leveraged in order to discriminate and discover different activity patterns caused by such factors. The use of DPM leads us to discover the appropriate number of the typical daily patterns automatically. Experimental result using long-term count data shows that our model successfully and efficiently discovers typical daily patterns.