An inclusive society has a growing demand for support systems that can accommodate the diversity of human physical and mental functions and the diversity of living environments. This study focuses on preventing home accidents involving young children as a concrete example of methodologies for observing, modeling, and controlling "living situations," which are complex systems composed of people and their environments, where interactions between people and environments change over time. Although previous research has proposed technologies for recognizing children’s behaviors and detecting environmental risks, few have integrated behavior and environment to predict accident pathways and time to occurrence. In this study, the authors propose a system that integrates home data scanned in three dimensions acquired through smartphones, child position data obtained using posture recognition technology, and epidemiological accident databases (N=358,715 cases) collected by the Tokyo Fire Department in Japan. The system estimates accident risks based on the relative positions of furniture and children, and visualizes the predicted paths and expected arrival time to high-risk objects in real-time. The evaluation based on national accident statistics confirmed that the system’s object recognition covers approximately 70% of furniture types involved in serious accidents. By conducting experiments for a six-year-old child and 55 three-dimensional scanned data obtained in actual living environments, the authors also demonstrate the feasibility of risk visualization considering the individuality and dynamic nature of living environments.

Falls among older adults are a serious global challenge. The World Health Organization strongly recommends gait, balance, and functional training, Tai Chi, or home assessment and modification to prevent falls among older people, but these strategies have not changed over the past few decades. The purpose of the present study is to propose an innovative approach to fall prevention naturally embedded in the environment. We first suggest new methods for simplifying and understanding the relationship between falls and everyday life, and describe this relationship as a knowledge graph using emergency transport data on falls among the aged. As a result of a cluster analysis using knowledge graphs, we identified whole-body balance as a key factor and living environmental determinant of falls. We then propose the concept of a homeostatic spatial system and discuss our development of seven homeostatic products as a proof of concept. Finally, we develop an evaluation system to visualize specific locations in a defined area where people maintain whole-body balance using their hands or other body parts. Our findings confirm that the system is useful for evaluating how people change their behaviors to maintain whole-body balance based on homeostatic products and the living environment as a whole.