ACTIVITY ANALYSIS WITH HIDDEN MARKOV MODEL FOR AMBIENT ASSISTED LIVING

In an Ambient Assisted Living (AAL) project the activities of the user will be analyzed. The raw data is from a motion detector. Through data processing the huge amount of dynamic raw data was translated to state data. With hidden Markov model, forward algorithm to analyze these state data the daily activity model of the user was built. Thirdly by comparing the model with observed activity sequences, and finding out the similarities between them, defined the best adapt routine in the model. Furthermore an activity routine net was built and used to compare with the hidden Markov model.