# Routine Modeling with Time Series Metric Learning

## TL;DR

This paper introduces a novel approach to recognize human routines from inertial time series data using metric learning with a sequence-to-sequence architecture, enabling privacy-preserving activity pattern clustering.

## Contribution

It proposes SS2S, a sequence-to-sequence based metric learning model for routine recognition from inertial data, focusing on unsupervised clustering of daily activity patterns.

## Key findings

- Clustering with learned distance effectively recovers daily routines.
- The approach is non-intrusive and preserves user privacy.
- Experimental results validate the method's ability to identify routines.

## Abstract

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.