Daily activity learning from motion detector data for Ambient Assisted Living

In an intelligent environment one important task is to observe and analyze person´s daily activities. Through analyzing the corresponding time series sensor data the person´s daily activity model should be build. To build such a model some problems have to be overcome: the sensor data count increase sharp with time and the distribution of the data is dynamically according the person´s daily activities. In an Ambient Assisted Living (AAL) project we handle this kind of time series sensor data from a motion detector. At first we reduce the data count through a predefined threshold value and build data “states” in time interval. Secondly, we analyze the states using a hidden Markov model, the forward algorithm, and the Viterbi Algorithm to build the person´s daily activity model. To test the correctness of the model some special and random day´s activities routine will be given.