Build user daily activity model and model structure changing

Because of the activity dynamic it is a challenged work to build daily activity model of user. The goal of the paper is to build daily activity model of the user with high accuracy. At first the raw data derived from motion detector will be “translated” to state data, then use state split and merge to build the basic model. Thirdly in order to increase accuracy of the model the count of the state data increased by changing the parameter of the translator. Here we get another two activity models with higher accuracy. The changing of the count of the state data caused to the structure changing of the build activity model. We merge the states from same route of activity models, compare different models and find out the same activity trends of the user. On the other hand from the models with higher accuracy the hidden activity will be detected.