Falls in the elderly occur in everyday life. Although it is well known that falls occurs frequently based on emergency transportation data and so on, main preventive measures are installing handrails and training of physical functions, and their effectiveness is limited. It comes from not understanding actual environments, behaviors and situations while falling. Since accidents and daily life are inseparably connected to individual and individual living environment, it is necessary to understand everyday life in view of them. However, because it is difficult to do, we cannot consider concrete preventive measures. Because it is not only falls but also we cannot understand changing of behaviors and life by shift of physical functions, it is difficult to solve everyday life issues such as early detecting risks of accidents and caring. Recent developments of AI and IoT technologies are making it possible to constantly monitor our daily living environment. However, it is difficult to select sensors and its arrangements and develop functions for detecting behaviors and risk situations in consideration of individual environment due to their complexity and cost. To solve this problem, it is necessary to develop functions quickly for gathering necessary data for understanding living situations using simple sensor system configuration. In this research, we developed a method for developing functions for detecting life events such as interaction behaviors with objects for understanding living situations in consideration of individual and individual environment. The method is composed of RGBD camera and virtual sensors perform in 3D point cloud data for detecting living events. In this paper, we also describe the result of understanding actual living situations by applying the method and gathering actual data in nursing home and conventional home.