Abstract Sensor-based activity recognition aims to recognize users' activities from multi-dimensional streams of sensor readings received from ubiquitous sensors. It has been shown that data segmentation and feature extraction are two crucial steps in developing machine learning-based models for sensor-based activity recognition. However, most previous studies were only focused on the latter step by assuming that data segmentation is done in advance. In practice, on the one hand, doing data segmentation on sensory streams is very challenging. On the other hand, if data segmentation is considered as a pre-process, the errors in data segmentation may be propagated to latter steps. Therefore, in this paper, we propose a unified weakly-supervised framework based on kernel embedding of distributions to jointly segment sensor streams, extract powerful features from each segment, and train a final classifier for activity recognition. We further offer an accelerated version for large-scale data by utilizing the technique of random Fourier features. We conduct experiments on four benchmark datasets to verify the effectiveness and scalability of our proposed framework.

中文翻译：

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通过从分布中学习，弱监督的基于传感器的活动分割和识别

摘要 基于传感器的活动识别旨在从无处不在的传感器接收到的多维传感器读数流中识别用户的活动。已经表明，数据分割和特征提取是为基于传感器的活动识别开发基于机器学习的模型的两个关键步骤。然而，以前的大多数研究都假设数据分割是提前完成的，只关注后一步。在实践中，一方面，对感官流进行数据分割是非常具有挑战性的。另一方面，如果将数据分割视为预处理，则数据分割中的错误可能会传播到后面的步骤。因此，在本文中，我们提出了一个统一的弱监督框架，基于分布的内核嵌入来联合分割传感器流，从每个片段中提取强大的特征，并训练最终分类器进行活动识别。我们通过利用随机傅立叶特征技术进一步为大规模数据提供加速版本。我们对四个基准数据集进行了实验，以验证我们提出的框架的有效性和可扩展性。