Falls in older adults is one of the most important public health problems world-wide. In our previous works, we showed that first-person vision (FPV) data acquired by chest- and waist-mounted cameras have the potential to be utilized to (A) develop novel markerless deep models to estimate spatiotemporal gait parameters over time (e.g., step width) by localizing feet in 2D coordinate system of RGB frames (using optical flow and RGB streams) and (B) automatically identify environmental hazards (e.g., curbs, stairs, different terrains) that may lead to falling. In this paper, a summary of our recent FPV-based approaches for fall risk assessment in the wild are being discussed. These approaches aimed to eventually inform clinical decisions on the most appropriate prevention interventions to reduce fall incidence in older populations.
|Number of pages||1|
|Journal||Journal of Computational Vision and Imaging Systems|
|Publication status||Published - 2 Jan 2020|