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.