This study aims to develop a system that quantitatively evaluates and visualizes indoor areas prone to accidents from the perspective of accessibility by analyzing the reach postures of a digital human model. To obtain a feasible final posture using an optimization method, it is crucial to provide an appropriate initial posture. However, because reach postures vary dynamically depending on location, these postures exhibit discontinuous mechanical modes. 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.