Fall Riskscape

Older adults are vulnerable to falls in outdoor settings, posing serious risks to their health and quality of life. Identifying fall injuries and associated risk factors is crucial to enhancing the safety and well-being of older adults. This study investigates the association between built environment features and outdoor fall risks among older adults using data from 6,302 emergency dispatch cases in Jeonbuk Province, South Korea. By leveraging deep learning-based computer vision and a zero-inflated Poisson model, we will analyze half a million street view images to identify micro-scale streetscape features associated with outdoor fall incidents. This study will provide valuable evidence for urban planners and public health professionals to create safer, age-friendly streetscapes through targeted interventions to prevent and reduce outdoor fall incidents among older adults.

This research was funded by Korea Health Industry Development Institute (KHIDI) under Grant [HS23C0056] and the research fund of Hanyang University[HY-202400000003326].