Human fall detection on embedded platform using depth maps and wireless accelerometer
Human fall detection on embedded platform using depth maps and wireless accelerometer
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DOI：
10.1016/j.cmpb.2014.09.005
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发表时间：
2014-12-01
影响因子：
6.1
通讯作者：
Kepski, Michal
中科院分区：
文献类型：
--
作者：
Kwolek, Bogdan;Kepski, Michal
由于老龄化社会的一个主要公共卫生问题，对低成本跌倒检测系统有相当大的需求。老年人不接受当前可用解决方案的主要原因之一是仅使用惯性传感器的跌倒探测器会产生过多的误报。这意味着一些日常活动被错误地标记为秋天，这反过来又导致用户感到沮丧。在本文中，我们介绍了如何设计和实现一个低成本系统，以非常低的误报率进行可靠的跌倒检测。跌倒检测是根据加速度数据和深度图来完成的。三轴加速度计用于指示潜在的跌倒以及指示人是否在运动。如果测量的加速度高于假定的阈值，算法会提取人员，计算特征，然后执行基于 SVM 的分类器来验证跌倒警报。它是一个 365/7/24 嵌入式系统，允许不引人注目的跌倒检测并保护用户的隐私。 (C) 2014 Elsevier Ireland Ltd. 保留所有权利。
Since a major public health problem in an aging society, there is considerable demand for low-cost fall detection systems. One of the main reasons for non-acceptance of the currently available solutions by seniors is that the fall detectors using only inertial sensors generate too much false alarms. This means that some daily activities are erroneously signaled as fall, which in turn leads to frustration of the users. In this paper we present how to design and implement a low-cost system for reliable fall detection with very low false alarm ratio. The detection of the fall is done on the basis of accelerometric data and depth maps. A tri-axial accelerometer is used to indicate the potential fall as well as to indicate whether the person is in motion. If the measured acceleration is higher than an assumed threshold value, the algorithm extracts the person, calculates the features and then executes the SVM-based classifier to authenticate the fall alarm. It is a 365/7/24 embedded system permitting unobtrusive fall detection as well as preserving privacy of the user. (C) 2014 Elsevier Ireland Ltd. All rights reserved.