@article{ref1,
title="Fall detection for elderly using anatomical-plane-based representation",
journal="Conference proceedings - IEEE engineering in medicine and biology society",
year="2014",
author="Alazrai, Rami and Zmily, Ahmad and Mowafi, Yaser",
volume="2014",
number="",
pages="5916-5919",
abstract="Falls are a common cause of injuries and traumas for elderly and could be life threatening. Delivering a prompt medical support after a fall is essential to prevent lasting injuries. Therefore, effective fall detection could provide urgent support and dramatically reduce the risk of such mishaps. In this paper, we propose a hierarchical classification framework based on a novel anatomical-plane-based representation for elderly fall detection. The framework obtains human skeletal joints, using Microsoft Kinect sensors, and transforms them to a human representation. The representation is then utilized to classify the sensor input sequences and provide a semantic meaning of different human activities. Evaluation results of the proposed framework, using real case scenarios, demonstrate the efficacy of the framework in providing a feasible approach towards accurately detecting elderly falls.<p /> <p>Language: en</p>",
language="en",
issn="1557-170X",
doi="10.1109/EMBC.2014.6944975",
url="http://dx.doi.org/10.1109/EMBC.2014.6944975"
}