Behavior prediction from trajectories in a house by estimating transition model using stay points
In this paper we propose a novel method for predicting resident's behaviors in a house from one's movement trajectories. The method consists of 1) segmentation of trajectory data into staying or moving and classification of the segments and 2) prediction by time-series association rules from transition events of each segment. The method predicts the start time of target behaviors for daily life support, such as eating, taking a bath etc. The time lag between the prediction and the target behavior can be set up manually, thus the method is adaptable to a variety of supporting systems. The experimental results using real residents' trajectory data of almost two years demonstrate that prediction of behaviors by the proposed method is feasible.