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Human activity modeling and prediction for assisting appliance operations

Recent increase of advanced appliances at home requires lots of user's operations for setting them in a desired state. Although many appliances have been developed which can adapt to the state of a room and a person, they are mainly based on simple state values such as the room temperature. More intelligent assistance will be needed for controlling electronic appliances such as a TV or an audio system. This paper describes a method of modeling human activities and predicting them for controlling appliances. We have developed an experimental system in a room with furniture and appliances. The system observes human motions and appliance operations and compiles them into actions, and then constructs a model of possible action sequences, which is further used for human action prediction. We test two prediction methods: a probabilistic action graph-based and an SVM (support vector machine)-based. We evaluate the methods using actual observation data for over fifty days in total. We also implement an on-line appliance control system as a proof-of-concept.