Ubiquitous sensor-based human behaviour recognition using the spatio-temporal representation of user states
Ubiquitous sensor-based human behaviour recognition using the spatio-temporal representation of user states
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DOI：
10.1504/ijwmc.2008.019717
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发表时间：
2008-07
期刊：
影响因子：
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通讯作者：
Yoshinori Isoda;S. Kurakake;K. Imai
中科院分区：
文献类型：
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作者：
Yoshinori Isoda;S. Kurakake;K. Imai
基于位置的上下文感知应用已经有了很多研究。然而，对一个人的活动的任何描述都必须包括时间方面和位置方面。因此，在创建增强的用户活动支持系统时，从时空约束的角度考虑用户的上下文是很重要的。在本文中，我们提出了一个用户活动支持系统，该系统采用状态序列描述方案来描述用户的上下文。在这个方案中，每个状态都被描述为用户和对象之间的时空关系。典型的状态序列存储为用户执行的活动模型。通过使用由称为C4.5的机器学习算法构建的决策树，传感器和射频识别标签（RFID标签）测量的用户活动的每个片段被分类为一种状态。然后通过将检测到的状态序列与存储的任务模型相匹配来获得用户的上下文。为了验证这个系统，我们开发了一个包含各种嵌入式传感器和rfid标签物体的实验屋。在评估了系统的性能后，我们得出结论，我们的系统是获取用户时空背景的有效方法。
There has been much research on location-based context-aware applications. However, any description of a person's activities must include a temporal aspect as well as a location aspect. Therefore, it is important when creating enhanced user activity support systems to consider the user's context in terms of spatio-temporal constraints. In this paper, we propose a user activity support system that employs a state sequence description scheme to describe the user's context. In this scheme, each state is described as a spatio-temporal relationship between the user and objects. Typical sequences of states are stored as models of activities performed by a user. Each segment of user activities measured by the sensors and the Radio Frequency Identification tags (RFID tags) is classified into a state by using a decision tree constructed by the machine learning algorithm called C4.5. The user's context is then obtained by matching the detected state series to a stored task model. To validate this system, we have developed an experimental house containing various embedded sensors and RFID-tagged objects. Having evaluated the performance of the proposed system, we conclude that our system is an effective way of acquiring the user's spatio-temporal context.