In this paper, we address the problem of recognizing the semantic human activities through the analysis of large dataset collected from users’ sensor-based smartphones. Our approach is unique in terms of covering a large number of activities that users could possibly engage in, and considering the multi-level-based classification model. Our model has three properties that never seemed to be addressed by existing approaches dealing with the same problem. These are: (1) comprehensiveness — in terms of the activity set, (2) accuracy — in terms of the activity classification, and (3) applicability — in terms of flexibility in being applied in real-life settings. Current approaches do not tackle all these properties. When tested on realistic dataset, our multi-level-based model achieved promising results despite the large number of activities being considered. When compared to similar approaches, our approach achieved comparable results in terms of accuracy and outperformed them in terms of the activity types, environment and settings covered, comprehensiveness, and applicability.

中文翻译：

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一种新的基于语义的智能手机活动识别多级分类方法

在本文中，我们通过分析从用户基于传感器的智能手机收集的大型数据集来解决识别语义人类活动的问题。我们的方法在涵盖用户可能参与的大量活动以及考虑基于多层次的分类模型方面是独一无二的。我们的模型具有处理相同问题的现有方法似乎从未解决的三个属性。它们是：（1）全面性——就活动集而言，（2）准确性——就活动分类而言，以及（3）适用性——就应用于现实生活环境的灵活性而言。当前的方法并没有解决所有这些属性。在真实数据集上进行测试时，尽管正在考虑大量活动，但我们基于多层次的模型取得了可喜的成果。与类似方法相比，我们的方法在准确性方面取得了可比的结果，在活动类型、涵盖的环境和设置、全面性和适用性方面优于它们。