状況と行動の因果関係に着目した人間の行動モデル化手法

HSMMの適用による時系列データの変化速度を考慮したモデル化

抄録

We have proposed a modeling method of human actions based on the causality between a situation and an action. In this method, a human action rule is expressed by an If-the-rule style, assumed that a person changes his current action to the next one according to the situation around him. In the previous method, a human action and a situation in a human action rule is modeled with a Hidden Markov Model(HMM). HMM is one of powerful tools for modeling time series data, but it ignores the change speed of time series data. In addition, time series data on human actions and situations are classified by Continuous Dynamic Programming. This means that two types of criteria should be set for modeling. In order to overcome these problems, we propose a new modeling method of human actions with Hidden Semi-Markov Model(HSMM) in this paper. In the proposed method, both clustering and modeling of time series data are executed with HSMM. The usefulness of the proposed method is discussed through some modeling results of human actions on operating a radio-controlled vehicle.