Assessing the quality of activities in a smart environment.
Assessing the quality of activities in a smart environment.
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评估智能环境中的活动质量。
DOI：
10.3414/me0592
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发表时间：
2009
影响因子：
1.7
通讯作者：
Schmitter-Edgecombe M
中科院分区：
文献类型：
--
作者：
Cook DJ;Schmitter-Edgecombe M
普适计算技术可以提供有价值的健康监测和辅助技术，帮助个人在自己家中独立生活。作为这项技术的关键部分，我们的目标是设计软件算法来识别和评估个人在自己家中进行的日常生活活动的一致性。我们设计了算法，为每一类活动自动学习马尔可夫模型。这些模型用于识别在智能家居中执行的活动，并识别执行活动中的错误和不一致。我们使用从60名志愿者那里收集的数据来验证我们的方法，这些志愿者在我们的智能公寓测试台上进行了一系列活动。结果表明，该算法正确地标记了活动，并成功地评估了执行任务的完整性和一致性。我们的研究结果表明，活动识别和评估可以使用机器学习算法和智能家居技术实现自动化。这些算法将有助于实现远程健康监测和干预的自动化。
Pervasive computing technology can provide valuable health monitoring and assistance technology to help individuals live independent lives in their own homes. As a critical part of this technology, our objective is to design software algorithms that recognize and assess the consistency of Activities of Daily Living that individuals perform in their own homes. We have designed algorithms that automatically learn Markov models for each class of activity. These models are used to recognize activities that are performed in a smart home and to identify errors and inconsistencies in the performed activity. We validate our approach using data collected from 60 volunteers who performed a series of activities in our smart apartment testbed. The results indicate that the algorithms correctly label the activities and successfully assess the completeness and consistency of the performed task. Our results indicate that activity recognition and assessment can be automated using machine learning algorithms and smart home technology. These algorithms will be useful for automating remote health monitoring and interventions.