Smartphones have emerged as a revolutionary technology for monitoring everyday life, and they have played an important role in Human Activity Recognition (HAR) due to its ubiquity. The sensors embedded in these devices allows recognizing human behaviors using machine learning techniques. However, not all solutions are feasible for implementation in smartphones, mainly because of its high computational cost. In this context, the proposed method, called HAR-SR, introduces information theory quantifiers as new features extracted from sensors data to create simple activity classification models, increasing in this way the efficiency in terms of computational cost. Three public databases (SHOAIB, UCI, WISDM) are used in the evaluation process. The results have shown that HAR-SR can classify activities with 93% accuracy when using a leave-one-subject-out cross-validation procedure (LOSO).

中文翻译：

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基于信息论的智能手机轻量级人类活动识别方法。

智能手机已经成为监视日常生活的革命性技术，并且由于其无处不在而在人类活动识别（HAR）中发挥了重要作用。这些设备中嵌入的传感器允许使用机器学习技术来识别人类行为。但是，并非所有解决方案都可在智能手机中实现，主要是因为其计算成本高。在这种情况下，所提出的方法称为HAR-SR，它引入了信息理论量词作为从传感器数据中提取的新特征，以创建简单的活动分类模型，从而以计算成本的方式提高了效率。评估过程中使用了三个公共数据库（SHOAIB，UCI，WISDM）。