A probabilistic approach for modeling human behavior in smart environments

Abstract

In order to act intelligently, a smart environment needs to have a notion about its users. Hidden Markov models are especially suited to recognize for example the state of a meeting in a smart meeting room, as they can cope with the noisy and intermittent sensor values. However, modeling the user behavior as an HMM is challenging, because of the high degrees of freedom the users have when acting in such a smart environment. Therefore, we compare two methods that ease the automatic generation of HMM and express the human behavior. © 2009 Springer Berlin Heidelberg.