%0 Conference Proceedings
%T Good-Eye: A Combined Computer-Vision and Physiological-Sensor Based Device for Full-Proof Prediction and Detection of Fall of Adults
%+ University of North Texas (UNT)
%A Rachakonda, Laavanya
%A Sharma, Akshay
%A Mohanty, Saraju, P.
%A Kougianos, Elias
%Z Part 6: Smart System Design and IoT Education
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 2nd IFIP International Internet of Things Conference (IFIPIoT)
%C Tampa, FL, United States
%Y Augusto Casaca
%Y Srinivas Katkoori
%Y Sandip Ray
%Y Leon Strous
%I Springer International Publishing
%3 Internet of Things. A Confluence of Many Disciplines
%V AICT-574
%P 273-288
%8 2019-10-31
%D 2019
%R 10.1007/978-3-030-43605-6_16
%K Internet of Things (IoT)
%K Smart healthcare
%K Healthcare cyber-physical system (H-CPS)
%K Fall detection
%K Elderly falls
%K Edge computing
%Z Computer Science [cs]Conference papers
%X It is imperative to find the most accurate way to detect falls in elders to help mitigate the disastrous effects of such unfortunate injuries. In order to mitigate fall related accidents, we propose the Good-Eye System, an Internet of Things (IoT) enabled Edge Level Device which works when there is an orientation change detected by a camera, and monitors physiological signal parameters. If the observed change is greater than the set threshold, the user is notified with information regarding a prediction of fall or a detection of fall, using LED lights. The Good-Eye System has a remote wall-attached camera to monitor continuously the subject as long as the person is in a room, along with a camera attached to a wearable to increase the accuracy of the model. The observed accuracy of the Good-Eye System as a whole is approximately 95%.
%G English
%2 https://inria.hal.science/hal-03371597/document
%2 https://inria.hal.science/hal-03371597/file/496697_1_En_16_Chapter.pdf
%L hal-03371597
%U https://inria.hal.science/hal-03371597
%~ IFIP
%~ IFIP-AICT
%~ IFIP-IOT
%~ IFIP-AICT-574