科研之友
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摘要
One of the many services that intelligent systems can provide is the automated assessment of resident well-being. We hypothesize that the functional health of individuals, or ability of individuals to perform activities independently without assistance, can be estimated by tracking their activities using smart home technologies. In this paper, we introduce a machine-learning-based method to assess activity quality in smart homes. To validate our approach, we quantify activity quality for 179 volunteer participants who performed a complex, interweaved set of activities in our smart home apartment. We compare our automated assessment of task quality with direct observation scores. We also assess the ability of machine-learning techniques to predict the cognitive health of the participants based on these automated scores. We believe that this capability is an important step in understanding everyday functional health of individuals in their home environments.
- 出版日期2013-11
