Modeling Visit Behaviour in Smart Homes using Unsupervised Learning

Many algorithms on health monitoring from ambient sensor networks assume that only a single person is present in the home. We present an unsupervised method that models visit behaviour. A Markov modulated multidimensional non-homogeneous Poisson process (M3P2) is described that allows us to model weekly and daily variations and to combine multiple data streams, namely the front-door sensor transitions and the general sensor transitions. The results from nine months of sensor data collected in the apartment of an elderly person show that our model outperforms the standard Markov modulated Poisson process (MMPP).