@article{ref1,
title="Acoustic fall detection using one-class classifiers",
journal="Conference proceedings - IEEE engineering in medicine and biology society",
year="2009",
author="Popescu, Mihail and Mahnot, Abhishek",
volume="1",
number="",
pages="3505-3508",
abstract="Falling represents a major health concern for the elderly. To address this concern we proposed in a previous paper an acoustic fall detection system, FADE, composed of a microphone array and a motion detector. FADE may help the elderly living alone by alerting a caregiver as soon as a fall is detected. A crucial component of FADE is the classification software that labels an event as a fall or part of the daily routine based on its sound signature. A major challenge in the design of the classifier is that it is almost impossible to obtain realistic fall sound signatures for training purposes. To address this problem we investigate a type of classifier, one-class classifier, that requires only examples from one class (i.e., non-fall sounds) for training. In our experiments we used three one-class (OC) classifiers: nearest neighbor (OCNN), SVM (OCSVM) and Gaussian mixture (OCGM). We compared the results of OC to the regular (two-class) classifiers on two datasets.<p /> <p>Language: en</p>",
language="en",
issn="1557-170X",
doi="10.1109/IEMBS.2009.5334521",
url="http://dx.doi.org/10.1109/IEMBS.2009.5334521"
}