The ongoing technological development in the fields of sensors,
actuators as well as embedded systems leads to more and more
complex and larger building automation systems. These systems allow
ever-better observations of activities in buildings with a rapid
growing number of possible applications. This work investigates how
statistical methods can be applied to (future) building automation
systems to recognize erroneous behavior and to extract semantic and
context information from sensor data. A hierarchical model
structure based on hidden Markov models is proposed to establish a
framework for learning about daily routines. 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. This
book is of interest for researchers active in science and
development of future context aware system for surveillance,
observation, or ambient assistance as well as for all individuals
interested in trends in building automation.
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