@inbook{5f333e3a01ef450d84b53e48a249b289,
title = "Automatic estimation of pose and falls in videos using computer vision model",
abstract = "Human pose estimation is defined as the process of locating joints of a person or a crowd given an image or video. Currently, this estimation is widely used for the evaluation of athletes, workers, and the monitoring of patients in clinical settings. However, human pose estimation is not an easy task as it requires experts to manually assess the person{\textquoteright}s position by using specialized equipment such as e-health devices (watches, bands, handles), markers, and high-cost cameras to monitor a limited scenario. The main goal of this article is to evaluate a marker-less low-cost computer vision system to get the automatic estimation of poses and fall detection on video by calculating the person{\textquoteright}s joint angle with a high level of adaptability to any space. The proposed model is the first step in the construction of a tool that allows monitoring and generating alerts to prevent falls at home and clinical settings.",
author = "{Calvache Brice{\~n}o}, {Daniela Andrea} and Bernal, {Hern{\'a}n A.} and Guar{\'i}n, {Juan F.} and Karen Agu{\'i}a and Orjuela-Ca{\~n}{\'o}n, {Alvaro D.} and Perdomo, {Oscar J.}",
note = "Publisher Copyright: {\textcopyright} 2020 SPIE Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 16th International Symposium on Medical Information Processing and Analysis 2020 ; Conference date: 03-10-2020 Through 04-10-2020",
year = "2020",
month = nov,
day = "3",
doi = "10.1117/12.2579615",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Eduardo Romero and Natasha Lepore and Jorge Brieva and Marius Linguraru",
booktitle = "16th International Symposium on Medical Information Processing and Analysis",
address = "United States",
}