Detection of unusual human behavior in intelligent house

This paper describes a model, based on a Markov process model, of daily human behavior in an intelligent house where human behavior is observed with small motion detectors. The number of sensor states is reduced to a few dozen by a vector quantization method, and transitions within this reduced set of states are observed. Then, the state transition probability and the transition duration time distribution are used as the templates of daily human activity. The validity of those templates is evaluated by detecting unusual human behavior in three sets of different human behavior data. Successful detection of unusual behavior without any a priori knowledge shows the effectiveness of probabilistic human behavior description in the intelligent house.