Monitoring Health by Detecting Drifts and Outliers for a Smart Environment Inhabitant 1
Monitoring Health by Detecting Drifts and Outliers for a Smart Environment Inhabitant 1
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通过检测智能环境居民的偏差和异常值来监测健康状况 1
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
Vikramaditya R. Jakkula
中科院分区：
文献类型：
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作者：
Gaurav Jain;D. Cook;Vikramaditya R. Jakkula
对许多人来说，家是避难所。对于那些需要特殊医疗护理的人来说，他们可能需要离开家来满足他们的医疗需求。随着人口老龄化，这一群体的比例正在增加，其影响是昂贵的，也令人不满意。我们没有意识到，许多残疾人在家庭自动辅助和健康监测的帮助下，可以在自己的家中独立生活。为了实现这一目标，必须开发强有力的方法来收集相关数据并对其进行处理，以发现和（或）预测具有威胁性的长期趋势或迫在眉睫的危机。这项工作的主要目标是设计使用基于代理的智能家居技术来提供这种家庭健康监测和帮助的技术。具体来说，我们解决了以下技术挑战：1)识别生活方式趋势，2)检测当前数据中的异常，以及3)设计提醒辅助系统。我们在MavHome项目中讨论了这样一个智能环境的实现，并介绍了在模拟和公寓环境中与志愿者一起测试这些技术的结果。
To many people, home is a sanctuary. For those people who need sp cial medical care, they may need to be pulled out of their home to meet th ir medical needs. As the population ages, the percentage of people in th is group is increasing and the effects are expensive as well as unsatisfying. We hyp ot esize that many people with disabilities can lead independent lives in thei r own homes with the aid of at-home automated assistance and health monitoring. In o rder to accomplish this, robust methods must be developed to collect relevant dat a and process it to detect and/or predict threatening long-term trends or immedi at crises. The main objective of this work is to design techniques for usi ng agent-based smart home technologies to provide this at-home health monitori g and assistance. Specifically, we address the following technologica l ch llenges: 1) identifying lifestyle trends, 2) detecting anomalies in current da ta, nd 3) designing a reminder assistance system. We discuss one such smart environme nt implementation in the MavHome project and present results from testing t hese techniques in simulation and with a volunteer in an apartment setting.