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Detecting Walking in Synchrony Through Smartphone Accelerometer and Wi-Fi Traces

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Ambient Intelligence (AmI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8850))

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Abstract

Social interactions play an important role in the overall well-being. Current practice of monitoring social interactions through questionnaires and surveys is inadequate due to recall bias, memory dependence and high end-user effort. However, sensing capabilities of smart-phones can play a significant role in automatic detection of social interactions. In this paper, we describe our method of detecting interactions between people, specifically focusing on interactions that occur in synchrony, such as walking. Walking together between subjects is an important aspect of social activity and thus can be used to provide a better insight into social interaction patterns. For this work, we rely on sampling smartphone accelerometer and Wi-Fi sensors only. We analyse Wi-Fi and accelerometer data separately and combine them to detect walking in synchrony. The results show that from seven days of monitoring using seven subjects in real-life setting, we achieve 99% accuracy, 77.2% precision and 90.2% recall detection rates when combining both modalities.

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Author information

Authors and Affiliations

  1. CREATE-NET, Via alla Cascata 56D, Trento, Italia

    Enrique Garcia-Ceja, Venet Osmani, Alban Maxhuni & Oscar Mayora

  2. Tecnológico de Monterrey, Campus Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey, N.L., México

    Enrique Garcia-Ceja

Authors
  1. Enrique Garcia-Ceja
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  2. Venet Osmani
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  3. Alban Maxhuni
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  4. Oscar Mayora
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Corresponding author

Correspondence to Venet Osmani .

Editor information

Editors and Affiliations

  1. Technische Universiteit Eindhoven, Eindhoven, The Netherlands

    Emile Aarts

  2. Philips Research Europe, Eindhoven, The Netherlands

    Boris de Ruyter

  3. Technische Universiteit Eindhoven, Eindhoven, The Netherlands

    Panos Markopoulos

  4. Philips Research, Eindhoven, The Netherlands

    Evert van Loenen

  5. Fraunhofer IGD, Darmstadt, Germany

    Reiner Wichert

  6. Technische Universiteit Eindhoven, Eindhoven, The Netherlands

    Ben Schouten

  7. Technische Universiteit Eindhoven, Eindhoven, The Netherlands

    Jacques Terken

  8. Waag Society, Amsterdam, The Netherlands

    Rob Van Kranenburg

  9. Technische Universiteit Eindhoven, Eindhoven, The Netherlands

    Elke Den Ouden

  10. University College Dublin, Dublin 4, Ireland

    Gregory O'Hare

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© 2014 Springer International Publishing Switzerland

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Garcia-Ceja, E., Osmani, V., Maxhuni, A., Mayora, O. (2014). Detecting Walking in Synchrony Through Smartphone Accelerometer and Wi-Fi Traces. In: Aarts, E., et al. Ambient Intelligence. AmI 2014. Lecture Notes in Computer Science(), vol 8850. Springer, Cham. https://doi.org/10.1007/978-3-319-14112-1_3

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  • DOI: https://doi.org/10.1007/978-3-319-14112-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14111-4

  • Online ISBN: 978-3-319-14112-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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