Behavior Learning in Dwelling Environments With Hidden Markov Models

Building automation systems (BASs) have seen widespread distribution also in private residences over the past few years. The ongoing technological developments in the fields of sensors, actuators, as well as embedded systems lead to more and more complex and larger systems. These systems allow ever-better observations of activities in buildings with a rapidly growing number of possible applications. Unfortunately, control systems with lots of parameters, which would be normally utilized, are hard to describe and-from a context-deriving view-hard to understand with standard control engineering techniques. This paper presents an approach to how statistical methods can be applied to (future) BASs to extract semantic and context information from sensor data. A hierarchical model structure based on hidden Markov models is proposed to establish a framework. The lower levels of the model structure are used to observe the sensor values themselves, whereas the higher levels provide a basis for the semantic interpretation of what is happening in the building. Ultimately, the system should be able to give a condensed overview of the daily routine of a sensor or the process that the sensor observes. While knowing the context of the sensor, a human operator can easily interpret the result.