Ubiquitous sensors based human behavior modeling and recognition using a spatio-temporal representation of user states

There has been much research on user activity assistance applications using the location of users and objects as context. However people´s activities are described in terms of time sequence aspect in addition to location aspect. Therefore, it is important for enhanced user activity support systems to consider the user´s context in terms of spatio-temporal constraints. In this paper, we propose a user activity assistance system that employs a state sequence description scheme to describe the user´s contexts. In this scheme, each state is described as a spatio-temporal relationship between the user and objects in the real world. Typical sets of states are stored as models of tasks performed by a user. To try out this system, we have developed an experimental house containing various embedded sensors and RFID-tagged objects. Each state is detected by a decision tree constructed by a C4.5 algorithm using the output of the sensors and the RFID tags. The user´s context is obtained by matching the detected state series to a task model. Having evaluated the performance of the proposed system in this experimental house, we conclude that our system is an effective way of acquiring the user´s spatio-temporal context.