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.
翻译:传统上,人类活动的自动承认是通过对有限几组具体活动的监督学习算法进行的,这项工作提议承认经常活动模式,称为例行活动,而不是精确界定的活动。例行活动的模型化被定义为一个计量学习问题,并提议一个建筑,称为SS2S,以顺序和顺序模式为基础,以学习时间序列之间的距离。这一方法只依靠惯性数据,因此不侵入性,并保护隐私。实验结果显示,以学习距离提供的组合算法能够恢复日常的例行程序。