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Weakly-supervised sensor-based activity segmentation and recognition via learning from distributions

机译：基于传感器的传感器的活动分割和通过分布学习的识别

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摘要

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.
机译：基于传感器的活动识别旨在识别来自从普遍传感器接收的传感器读数的多维流中的用户活动。已经表明，数据分割和特征提取是开发基于机器学习的基于传感器的活动识别的模型的两个关键步骤。然而，最先前的研究仅通过假设提前完成数据分割来专注于后一步。在实践中，一方面，在感官流上进行数据分割非常具有挑战性。另一方面，如果将数据分割被认为是预处理，则数据分段中的错误可以传播到后一步。因此，在本文中，我们提出了一种基于内核嵌入分布到共同段传感器流的统一弱监督的框架，从每个段中提取强大的特征并培训最终分类器以进行活动识别。我们通过利用随机傅里叶功能技术，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实验，以验证我们提出框架的有效性和可扩展性。

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