Wearable Vision Detection of Environmental Fall Risks using Convolutional Neural Networks
This addresses fall prevention for seniors by providing objective risk detection from wearable cameras, but it appears incremental as it applies existing CNN methods to a new domain-specific dataset.
The paper tackled the problem of detecting environmental fall risks for seniors by classifying 12 hazard types from first-person videos using a convolutional neural network, achieving an initial mean square error of 8%.
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.