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Context-aware middleware for pervasive elderly homecare

机译:用于无处不在的老年人家庭护理的上下文感知中间件

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摘要

The growing aging population faces a number of challenges, including rising medical cost, inadequate number of medical doctors and healthcare professionals, as well as higher incidence of misdiagnosis. There is an increasing demand for a better healthcare support for the elderly and one promising solution is the development of a context-aware middleware infrastructure for pervasive health/wellness-care. This allows the accurate and timely delivery of health/medical information among the patients, doctors and healthcare workers through a widespread deployment of wireless sensor networks and mobile devices. In this paper, we present our design and implementation of such a context-aware middleware for pervasive homecare (CAMPH). The middleware offers several key-enabling system services that consist of P2P-based context query processing, context reasoning for activity recognition and context-aware service management. It can be used to support the development and deployment of various homecare services for the elderly such as patient monitoring, location-based emergency response, anomalous daily activity detection, pervasive access to medical data and social networking. We have developed a prototype of the middleware and demonstrated the concept of providing a continuing-care to an elderly with the collaborative interactions spanning multiple physical spaces: person, home, office and clinic. The results of the prototype show that our middleware approach achieves good efficiency of context query processing and good accuracy of activity recognition.
机译:不断增长的老龄化人口面临着许多挑战,包括医疗成本上涨,医生和保健专业人员的数量不足以及误诊率更高。对老年人的更好的医疗支持的需求与日俱增,一个有前途的解决方案是开发用于普适健康/保健的情境感知中间件基础架构。通过无线传感器网络和移动设备的广泛部署,这可以在患者,医生和医护人员之间准确,及时地交付健康/医疗信息。在本文中,我们介绍了这种用于普适家庭护理(CAMPH)的上下文相关中间件的设计和实现。中间件提供了几种启用密钥的系统服务,这些服务包括基于P2P的上下文查询处理,活动识别的上下文推理和上下文感知的服务管理。它可用于支持各种针对老年人的家庭护理服务的开发和部署,例如患者监测,基于位置的紧急响应,异常日常活动检测,对医疗数据的广泛访问和社交网络。我们开发了中间件的原型,并演示了通过跨多个物理空间(人,家庭,办公室和诊所)的协作交互为老年人提供持续护理的概念。原型的结果表明,我们的中间件方法实现了上下文查询处理的良好效率和活动识别的良好准确性。

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