  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.
