@article{ref1,
title="Fall detection via human posture representation and support vector machine",
journal="International journal of distributed sensor networks",
year="2017",
author="Fan, Kaibo and Wang, Ping and Hu, Yan and Dou, Bingjie",
volume="13",
number="5",
pages="e1550147717707418-e1550147717707418",
abstract="Accidental falls of elderly people are a major cause of fatal injuries, especially for those living alone. We present a novel vision-based fall detection approach that analyzes an extracted human body using described human postures. First, a human body extracted by a background subtraction technique is located by a minimum area-enclosing ellipse. Then, a normalized directional histogram is developed around the center of the ellipse to represent a human posture by multi-directional statistical analysis. After that, 12 static and 8 dynamic features are derived from the normalized directional histogram. These features are fed into a directed acyclic graph support vector machine to distinguish four closely related human postures (standing, crouching, lying, and sitting). A fall-like accident is detected by counting the occurrences of lying postures in a short temporal window. After conducting majority voting, a fall event is determined by immobility verification. From the experimental results, an overall accuracy of 97.1% is obtained for recognition of the four postures, and only 1.0% of postures are misclassified as lying postures. Our fall detection system achieves up to 95.2% fall detection accuracy on a public fall dataset.<p /> <p>Language: en</p>",
language="en",
issn="1550-1329",
doi="10.1177/1550147717707418",
url="http://dx.doi.org/10.1177/1550147717707418"
}