The embedded sensors in widely used smartphones and other wearable devices make the data of human activities more accessible. However, recognizing different human activities from the wearable sensor data remains a challenging research problem in ubiquitous computing. One of the reasons is that the majority of the acquired data has no labels. In this paper, we present an unsupervised approach, which is based on the nature of human activity, to project the human activities into an embedding space in which similar activities will be located closely together. Using this, subsequent clustering algorithms can benefit from the embeddings, forming behavior clusters that represent the distinct activities performed by a person. Results of experiments on three labeled benchmark datasets demonstrate the effectiveness of the framework and show that our approach can help the clustering algorithm achieve improved performance in identifying and categorizing the underlying human activities compared to unsupervised techniques applied directly to the original data set.

翻译：广泛使用的智能手机及其他可穿戴设备中的嵌入式传感器，使得人类活动数据的获取更加便捷。然而，从可穿戴传感器数据中识别不同的人类活动，仍然是普适计算领域具有挑战性的研究问题。原因之一在于，所获取的大部分数据缺乏标签。本文提出了一种基于人类活动本质的无监督方法，将人类活动投影至嵌入空间，在该空间中，相似的活动将彼此紧密聚集。通过此方法，后续的聚类算法可利用嵌入向量形成行为簇，这些簇代表个人执行的不同活动。在三个带标签的基准数据集上的实验结果表明了该框架的有效性，并显示我们的方法相较于直接应用于原始数据集的无监督技术，能够帮助聚类算法在识别和分类潜在人类活动方面实现更优性能。