Predicting user Activities in the Sequence of Mobile Context for Ambient Intelligence Environment using Dynamic Bayesian Network

Recently, mobile devices became essential mediums in order to implement ambient intelligence. Since people can always keep these mobile devices, it is easy for them to collect diverse user information. Therefore, many research groups have attempted to provide useful services based on this ubiquitous information. This paper proposes a method to predict user activity in the sequence of mobile context. In order to conduct accurate prediction of activity among various patterns, we have considered user activity, place, time and day of week as mobile context. We have used dynamic Bayesian network to model the user activity patterns with this context, and learned the model of each individual to obtain better model. For experiments, we have collected the mobile logs of undergraduate students, and confirmed that the proposed method produced good performance.