Auvinet Edouard, Multon Franck, Saint-Arnaud Alain, Rousseau Jacqueline, Meunier Jean
Institute of Biomedical Engineering, University of Montreal, Montreal, QC, Canada.
IEEE Trans Inf Technol Biomed. 2011 Mar;15(2):290-300. doi: 10.1109/TITB.2010.2087385. Epub 2010 Oct 14.
According to the demographic evolution in industrialized countries, more and more elderly people will experience falls at home and will require emergency services. The main problem comes from fall-prone elderly living alone at home. To resolve this lack of safety, we propose a new method to detect falls at home, based on a multiple-cameras network for reconstructing the 3-D shape of people. Fall events are detected by analyzing the volume distribution along the vertical axis, and an alarm is triggered when the major part of this distribution is abnormally near the floor during a predefined period of time, which implies that a person has fallen on the floor. This method was validated with videos of a healthy subject who performed 24 realistic scenarios showing 22 fall events and 24 cofounding events (11 crouching position, 9 sitting position, and 4 lying on a sofa position) under several camera configurations, and achieved 99.7% sensitivity and specificity or better with four cameras or more. A real-time implementation using a graphic processing unit (GPU) reached 10 frames per second (fps) with 8 cameras, and 16 fps with 3 cameras.
根据工业化国家的人口结构演变，越来越多的老年人会在家中摔倒并需要紧急服务。主要问题来自于容易摔倒且独自在家居住的老年人。为了解决这种安全缺失问题，我们提出一种在家中检测摔倒的新方法，该方法基于一个用于重建人体三维形状的多摄像头网络。通过分析沿垂直轴的体积分布来检测摔倒事件，当在预定义时间段内该分布的主要部分异常靠近地面时，就会触发警报，这意味着有人摔倒在地上。该方法已通过一名健康受试者的视频进行验证，该受试者在几种摄像头配置下进行了24个真实场景的演示，其中包括22次摔倒事件和24次混淆事件（11次蹲姿、9次坐姿和4次躺在沙发上的姿势），使用四个或更多摄像头时，灵敏度和特异性达到了99.7%或更高。使用图形处理单元（GPU）的实时实现，在使用8个摄像头时达到了每秒10帧（fps），使用3个摄像头时达到了每秒16帧。
