Two HMM-based threshold models are suggested for recognition and incremental learning of scenario-oriented human behavior patterns. One is the expected behavior threshold model to discriminate if a monitored behavior pattern is normal or not. The other model is the registered behavior threshold model to detect whether such behavior pattern is already learned. If a behavior patten is detected as a new one, an HMM is generated to represent the pattern, and then the HMM is used to update behavior clusters by hierarchical clustering process.

Understanding situations has been regarded as a highly difficult task due to its complexity, especially in case of human-augmented ones where the contexts of situations are largely influenced by human activity. Even though the complexity of situations can be solved by modeling the real environment, it is not easy to model which covers uncertain problems of real world effectively. For this, this paper proposes a fusion technology for service-oriented context reasoning combining low-level sensory patterns and high-level semantic knowledge using Hidden Markov Model (HMM) and ontology. Integrated temporal reasoning using our approach enables a service robot to understand human-augmented situations immediately whenever the critical situation happens. Experimental results show that the proposed method can successfully extract situations from continuous sensory signals with 80% percent reliability.