Ariani Arni, Redmond Stephen J, Chang David, Lovell Nigel H
Graduate School of Biomedical Engineering, University of New South Wales, Sydney, 2052, Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:2115-8. doi: 10.1109/IEMBS.2010.5627202.
Falls and their related injuries are a major challenge facing elderly people. One serious issue related to falls among the elderly living at home is the 'long-lie' scenario, which is the inability to get up from the floor after a fall, followed by lying on the floor for 60 minutes, or more. Several studies of accelerometer and gyroscope-based wearable falls detection devices have been cited in the literature. However, when the subject moves around at night-time, such as making a trip from the bedroom to the toilet, it is unlikely that they will remember or even feel an inclination to wear such a device. This research will investigate the potential usefulness of an unobtrusive fall detection system, based on the use of passive infrared sensors (PIRs) and pressure mats (PMs), that will detect falls automatically by recognizing unusual activity sequences in the home environment; hence, decreasing the number of subjects suffering the 'long-lie' scenario after a fall. A Java-based wireless sensor network (WSN) simulation was developed. This simulation reads the room coordinates from a residential map, a path-finding algorithm (A*) simulates the subject's movement through the residential environment, and PIR and PM sensors respond in a binary manner to the subject's movement. The falls algorithm was tested for four scenarios; one scenario including activities of daily living (ADL) and three scenarios simulating falls. The simulator generates movements for ten elderly people (5 female and 5 male; age: 50-70 years; body mass index: 25.85-26.77 kg/m(2)). A decision tree based heuristic classification model is used to analyze the data and differentiate falls events from normal activities. The sensitivity, specificity and accuracy of the algorithm are 100%, 66.67% and 90.91%, respectively, across all tested scenarios.
跌倒及其相关伤害是老年人面临的一项重大挑战。与居家老年人跌倒相关的一个严重问题是“长时间躺卧”情况，即跌倒后无法从地板上起身，继而在地板上躺卧60分钟及以上。文献中已引用了多项关于基于加速度计和陀螺仪的可穿戴式跌倒检测设备的研究。然而，当受试者在夜间活动时，比如从卧室去卫生间，他们不太可能记得甚至有佩戴此类设备的意愿。本研究将调查一种基于被动红外传感器（PIR）和压力垫（PM）的不引人注意的跌倒检测系统的潜在效用，该系统将通过识别家庭环境中的异常活动序列来自动检测跌倒；从而减少跌倒后遭遇“长时间躺卧”情况的受试者数量。开发了一个基于Java的无线传感器网络（WSN）模拟程序。此模拟程序从住宅地图读取房间坐标，一种路径查找算法（A*）模拟受试者在住宅环境中的移动，PIR和PM传感器以二进制方式对受试者的移动做出响应。跌倒算法针对四种情况进行了测试；一种情况包括日常生活活动（ADL），三种情况模拟跌倒。模拟器为十名老年人（5名女性和5名男性；年龄：50 - 70岁；体重指数：25.85 - 26.77 kg/m²）生成移动数据。使用基于决策树的启发式分类模型来分析数据并区分跌倒事件与正常活动。在所有测试情况下，该算法的灵敏度、特异性和准确率分别为100%、66.67%和90.91%。
