Lee Tracy, Mihailidis Alex
Intelligent Assistive Technology and Systems Laboratory, Department of Occupational Therapy, University of Toronto, Canada.
J Telemed Telecare. 2005;11(4):194-8. doi: 10.1258/1357633054068946.
We have designed an intelligent emergency response system to detect falls in the home. It uses image-based sensors. A pilot study was conducted using 21 subjects to evaluate the efficacy and performance of the fall-detection component of the system. Trials were conducted in a mock-up bedroom setting, with a bed, a chair and other typical bedroom furnishings. A small digital videocamera was installed in the ceiling at a height of approximately 2.6 m. The digital camera covered an area of approximately 5.0 m x 3.8 m. The subjects were asked to assume a series of postures, namely walking/standing, sitting/lying down in an inactive zone, stooping, lying down in a 'stretched' position, and lying down in a 'tucked' position. These five scenarios were repeated three times by each subject in a random order. These test positions totalled 315 tasks with 126 fall-simulated tasks and 189 non-fall-simulated tasks. The system detected a fall on 77% of occasions and missed a fall on 23%. False alarms occurred on only 5% of occasions. The results encourage the potential use of a vision-based system to provide safety and security in the homes of the elderly.
我们设计了一个智能应急响应系统来检测家中的跌倒情况。该系统使用基于图像的传感器。我们进行了一项试点研究，招募了21名受试者来评估该系统跌倒检测组件的功效和性能。试验在一个模拟卧室环境中进行，里面有一张床、一把椅子和其他典型的卧室家具。在天花板上大约2.6米的高度安装了一台小型数码摄像机。这台数码摄像机覆盖的面积约为5.0米×3.8米。要求受试者摆出一系列姿势，即行走/站立、在非活动区域坐着/躺下、弯腰、以“伸展”姿势躺下以及以“蜷缩”姿势躺下。每个受试者以随机顺序将这五种场景重复三次。这些测试姿势总共包含315项任务，其中有126项模拟跌倒任务和189项非模拟跌倒任务。该系统在77%的情况下检测到了跌倒，23%的情况下未检测到跌倒。误报仅发生在5%的情况下。这些结果促使人们有可能使用基于视觉的系统为老年人家庭提供安全保障。
