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Routine Modeling with Time Series Metric Learning

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Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning (ICANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11728))

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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.

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Notes

  1. 1.

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Authors and Affiliations

  1. Orange Labs, Grenoble, France

    Paul Compagnon & Grégoire Lefebvre

  2. LIRIS, UMR 5205 CNRS INSA-Lyon, Lyon, France

    Paul Compagnon, Stefan Duffner & Christophe Garcia

Authors
  1. Paul Compagnon
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  2. Grégoire Lefebvre
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  3. Stefan Duffner
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  4. Christophe Garcia
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Corresponding author

Correspondence to Paul Compagnon .

Editor information

Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Fabian Theis

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Compagnon, P., Lefebvre, G., Duffner, S., Garcia, C. (2019). Routine Modeling with Time Series Metric Learning. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning. ICANN 2019. Lecture Notes in Computer Science(), vol 11728. Springer, Cham. https://doi.org/10.1007/978-3-030-30484-3_47

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  • DOI: https://doi.org/10.1007/978-3-030-30484-3_47

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