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Routine Modeling with Time Series Metric Learning

机译：时间序列度量学习的常规建模

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摘要

Traditionally, the automatic recognition of human activities is performed with supervised learning algorithms on limited sets of specific activities. This work proposes to recognize recurrent activity patterns, called routines, instead of precisely defined activities. The modeling of routines is defined as a metric learning problem, and an architecture, called SS2S, based on sequence-to-sequence models is proposed to learn a distance between time series. This approach only relies on inertial data and is thus non intrusive and preserves privacy. Experimental results show that a clustering algorithm provided with the learned distance is able to recover daily routines.
机译：传统上，人类活动的自动识别是通过监督学习算法对有限的一组特定活动进行的。这项工作建议识别周期性活动模式，称为例程，而不是精确定义的活动。例程的建模被定义为度量学习问题，并且提出了一种基于序列到序列模型的名为SS2S的体系结构，以学习时间序列之间的距离。这种方法仅依赖于惯性数据，因此是非侵入性的，并保留了隐私。实验结果表明，具有学习距离的聚类算法能够恢复日常工作。

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