Popescu Mihail, Mahnot Abhishek
Health Management and Informatics Department, University of Missouri, Columbia, MO 65211, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3505-8. doi: 10.1109/IEMBS.2009.5334521.
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.
跌倒对老年人来说是一个重大的健康问题。为了解决这一问题，我们在之前的一篇论文中提出了一种声学跌倒检测系统FADE，它由一个麦克风阵列和一个运动探测器组成。FADE可以通过在检测到跌倒后立即提醒护理人员来帮助独居老人。FADE的一个关键组件是分类软件，它根据事件的声音特征将其标记为跌倒或日常活动的一部分。分类器设计中的一个主要挑战是几乎不可能获得用于训练目的的真实跌倒声音特征。为了解决这个问题，我们研究了一种分类器，即单类分类器，它只需要一类（即非跌倒声音）的示例进行训练。在我们的实验中，我们使用了三种单类（OC）分类器：最近邻（OCNN）、支持向量机（OCSVM）和高斯混合（OCGM）。我们在两个数据集上比较了OC分类器与常规（两类）分类器的结果。
