Many intelligent systems that focus on the needs of a human require information about the activities that are being performed by the human. At the core of this capability is activity recognition. Activity recognition techniques have become robust but rarely scale to handle more than a few activities. They also rarely learn from more than one smart home data set because of inherent differences between labeling techniques. In this paper we investigate a data-driven approach to creating an activity taxonomy from sensor data found in disparate smart home datasets. We investigate how the resulting taxonomy can help analyze the relationship between classes of activities. We also analyze how the taxonomy can be used to scale activity recognition to a large number of activity classes and training datasets. We describe our approach and evaluate it on 34 smart home datasets. The results of the evaluation indicate that the hierarchical modeling can reduce training time while maintaining accuracy of the learned model.

中文翻译：

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学习预定义和发现的活动模式的分类

许多关注人类需求的智能系统需要有关人类正在执行的活动的信息。此功能的核心是活动识别。活动识别技术已经变得强大，但很少能扩展到处理多个活动。由于标记技术之间的固有差异，他们也很少从多个智能家居数据集中学习。在本文中，我们研究了一种数据驱动的方法，该方法可以根据不同智能家居数据集中的传感器数据创建活动分类法。我们调查所得分类法如何帮助分析活动类别之间的关系。我们还分析了如何使用分类法将活动识别扩展到大量活动类和训练数据集。我们描述了我们的方法并在 34 个智能家居数据集上对其进行了评估。评估结果表明，分层建模可以减少训练时间，同时保持学习模型的准确性。