  In this paper, a method to detect environmental hazards related to a fall
risk using a mobile vision system is proposed. First-person perspective videos
are proposed to provide objective evidence on cause and circumstances of
perturbed balance during activities of daily living, targeted to seniors. A
classification problem was defined with 12 total classes of potential fall
risks, including slope changes (e.g., stairs, curbs, ramps) and surfaces (e.g.,
gravel, grass, concrete). Data was collected using a chest-mounted GoPro
camera. We developed a convolutional neural network for automatic feature
extraction, reduction, and classification of frames. Initial results, with a
mean square error of 8%, are promising.
