Visualization of Indoor Reachability-Related Risks Using a Digital Human Model

Derivation of Initial Posture Candidates Based on Machine Learning Adapted to the Environment

Abstract

This study aims to develop a system that quantitatively evaluates and visualizes indoor areas that are both accessible to children and prone to accidents by analyzing the reach postures of a digital human model. The system supports caregivers in creating a safer environment. Since reach postures vary dynamically depending on location—for example, a child may support their body with one hand—these postures exhibit discontinuous mechanical modes. To optimize the final posture using an optimization method, it is crucial to provide an appropriate initial posture. Therefore, this study introduces a machine learning-based approach for estimating initial postures and quantitatively evaluates the preventive effects of existing accident prevention products, thereby verifying the effectiveness of the proposed system.