@article{ref1,
title="Feasibility of using floor vibration to detect human falls",
journal="International journal of environmental research and public health",
year="2021",
author="Shao, Yu and Wang, Xinyue and Song, Wenjie and Ilyas, Sobia and Guo, Haibo and Chang, Wen-Shao",
volume="18",
number="1",
pages="e200-e200",
abstract="With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health  problems. This study proposes a classification framework that uses floor vibrations  to detect fall events as well as distinguish different fall postures. A scaled  3D-printed model with twelve fully adjustable joints that can simulate human body  movement was built to generate human fall data. The mass proportion of a human body  takes was carefully studied and was reflected in the model. Object drops, human  falling tests were carried out and the vibration signature generated in the floor  was recorded for analyses. Machine learning algorithms including K-means algorithm  and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop,  human falls from different postures) were developed in this study. <br><br>RESULTS showed  that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern  recognition system in detecting human falls based on a machine learning approach.<p /> <p>Language: en</p>",
language="en",
issn="1661-7827",
doi="10.3390/ijerph18010200",
url="http://dx.doi.org/10.3390/ijerph18010200"
}