Wearable Vision Detection of Environmental Fall Risks using Convolutional Neural Networks

Abstract

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.