Weakly-supervised sensor-based activity segmentation and recognition via learning from distributions
Weakly-supervised sensor-based activity segmentation and recognition via learning from distributions
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DOI：
10.1016/j.artint.2020.103429
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
Hangwei Qian;Sinno Jialin Pan;C. Miao
中科院分区：
文献类型：
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作者：
Hangwei Qian;Sinno Jialin Pan;C. Miao
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。研究表明，数据分割和特征提取是建立基于机器学习的传感器活动识别模型的两个关键步骤。然而，以前的大多数研究都只关注后一步，假设数据分割是提前进行的。在实际应用中，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一个前处理，则数据分割中的误差可能会传播到后面的步骤。因此，本文提出了一种基于分布核嵌入的统一弱监督框架，用于对传感器流进行联合分割，从每个片段中提取强特征，并训练最终的分类器用于活动识别。我们进一步利用随机傅立叶特征技术，为大规模数据提供了一个加速版本。我们在四个基准数据集上进行了实验，以验证我们提出的框架的有效性和可扩展性。
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.