A smart home application to eldercare: current status and lessons learned.
A smart home application to eldercare: current status and lessons learned.
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DOI：
10.3233/thc-2009-0551
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发表时间：
2009-01-01
期刊：
影响因子：
--
通讯作者：
Keller, James
中科院分区：
文献类型：
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作者：
Skubic, Marjorie;Alexander, Gregory;Keller, James
为了解决人口老龄化问题，我们一直在研究用于监测家中老年人的传感器网络。在本文中，我们报告了正在进行的工作，其中无源传感器网络已安装在老年护理机构的 17 间公寓中。正在开发的网络包括简单的运动传感器、视频传感器以及捕捉睡眠不安以及脉搏和呼吸水平的床传感器。一些公寓的数据收集已经持续了两年多。传感器数据收集的寿命使我们能够研究数据并开发算法来识别跌倒等警报条件，并提取个人的典型日常活动模式。目标是捕获代表身体和认知健康状况的模式，然后识别活动模式何时开始偏离正常水平。在此过程中，我们努力尽早发现潜在问题，如果不及时处理，可能会导致严重的健康事件。我们描述了网络的组件，并展示了记录的传感器数据的示例以及健康事件的相关参考。还总结了我们在家中监测老年人的经验中遇到的挑战和吸取的教训。
To address an aging population, we have been investigating sensor networks for monitoring older adults in their homes. In this paper, we report ongoing work in which passive sensor networks have been installed in 17 apartments in an aging in place eldercare facility. The network under development includes simple motion sensors, video sensors, and a bed sensor that captures sleep restlessness and pulse and respiration levels. Data collection has been ongoing for over two years in some apartments. This longevity in sensor data collection is allowing us to study the data and develop algorithms for identifying alert conditions such as falls, as well as extracting typical daily activity patterns for an individual. The goal is to capture patterns representing physical and cognitive health conditions and then recognize when activity patterns begin to deviate from the norm. In doing so, we strive to provide early detection of potential problems which may lead to serious health events if left unattended. We describe the components of the network and show examples of logged sensor data with correlated references to health events. A summary is also included on the challenges encountered and the lessons learned as a result of our experiences in monitoring aging adults in their homes.