In recent years, the number of single elderly people has been increasing, and the needs of residents have been diversifying. Towards these backgrounds, we propose the concept of "Biofied bulding". The aim of Biofied Building is to create living spaces where residents can live safely, securely and comfortably. Small robots are used as an interface between residents and living space in Biofied Building. The aim of using robots is to sense the position and movement of residents in real time and providing feedback to them. However, the present control systems of the robot do not have enough functions to estimate the risk of accidents such as falls and choose the pathways which do not disturb residents. Therefore, the purpose of this research is to recognize and predict human behavior in a living space by using a robot to realize Biofied Building. In particular, we focus on the direction change motion, which is an important behavior in a living space, and extract the prediction parameters. In particular, it is reported that the direction change motion account for about 20% of gait during the daily life. Therefore, our research group decided to focus on direction change motion. In this study, we focused on the center of the head to extract parameters for prediction of the direction change motion. There are features in the velocity change of the center of the head compared with straight-line gait. There was a velocity amplification of the opposite direction of the direction change before the start of the motion. It is assumed that the shift of the center of mass make it to easier to step out to the direction of the turn.