Modeling and recognition method of human behaviors with multi-dimensional time series data

We propose a modeling and recognition method of human behaviors in this paper in order to realize such intelligent systems that can adapt humans, i.e. the systems that support humans by considering human behaviors, In the proposed method, we assume that a human changes his behavior according to the change of the situation around him, and this concept is expressed by If-Then-Rules, which is called behavior rules. In behavior rules, the change of the situation around a person is described by multi-dimensional time series sensing data which is modeled with Hidden Markov Model(HMM). To recognize the change of human behaviors, the optimal If-Then-Rule is chosen based on the current human behavior and similarity to the time series data of the situation obtained by sensors. As an example of human behaviors, human driving behaviors are considered, and a recognition system of human driving behaviors is constructed. The usefulness of the proposed method is examined through some experimental results with the constructed system.