Using Bayesian Networks for Daily Activity Prediction

In spite of the significant work that has been done to discover and recognize activities in the smart home research, less attention has been paid to predict the future activities that the resident is likely to perform. An activity prediction module can play a major role in design of a smart home. For instance, by taking advantage of an activity prediction module, a smart home can learn context-aware rules to prompt individuals to initiate important activities. In this paper, we propose an activity prediction approach using Bayesian networks. We propose a novel two-step inference process to predict the next activity features and then to predict the next activity label. We also propose an approach to predict the start time of the next activity which is based on modeling the relative start time of the predicted activity using the continuous normal distribution and outlier detection. We evaluate our proposed models using real data collected from two smart home apartments.