Modeling method of human action with HS considering its temporal and spatial differences

In this paper, a modeling method of human actions is proposed. This is because systems to assist human operations have been desired, which must have a certain human action model to recognize or support various kinds of human actions. In the proposed method, a human action model is statistically generated by extracted from enormous data obtained by long-term monitoring with sensors. Therefore, human actions can be modeled without previous knowledge. In addition, a human action model is generated considering the differences on not only spatial patterns but also temporal ones of human actions. This is because it is very significant to support human operations timely. In order to generate a human action model with the previous two features, a human action is modeled as a human action pattern expressed by Hidden Semi Markov Model(HSMM) and a If-Then-Rule in the proposed method. HSMM can model a time series data explicitly. It can also model the timing information of a change of a human action. In addition, the relationship between the situation and a human action is expressed by If-Then-Rule explicitly. Therefore, a human action model has high readability.