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.
@article{arxiv.1611.00684,
title = {Wearable Vision Detection of Environmental Fall Risks using Convolutional Neural Networks},
author = {Mina Nouredanesh and Andrew McCormick and Sunil L. Kukreja and James Tung},
journal= {arXiv preprint arXiv:1611.00684},
year = {2016}
}
Comments
Accepted paper-The 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016)