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International Conference on Mobile Computing, Applications, and Services

MobiCASE 2015: Mobile Computing, Applications, and Services pp 309–316Cite as

A Spatiotemporal Approach for Social Situation Recognition

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Abstract

The development of virtual personal assistants requires situation awareness. For this purpose, lightweight approaches for the processing of sensor data to derive situation information from available sensor data (e.g., mobile phone data) are required.

In this paper, we propose a spatiotemporal approach to derive situational information about social interactions only based on location and time, using data collected with off-the-shelf smartphones. We examine the approach, using location traces of 163 users collected over four weeks. The proposed spatiotemporal approach shows an average social situation recognition result of \(45.8\pm 23.2\,\%\) \(F_1\)-measure across the data set using Random Forest classifiers.

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Acknowledgments

This work has been funded by the LOEWE initiative (Hessen, Germany) within the NICER project.

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Authors and Affiliations

  1. Telecooperation Lab, Technische Universität Darmstadt, Darmstadt, Germany

    Christian Meurisch, Tahir Hussain, Artur Gogel, Benedikt Schmidt, Immanuel Schweizer & Max Mühlhäuser

Authors
  1. Christian Meurisch
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  2. Tahir Hussain
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  3. Artur Gogel
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  4. Benedikt Schmidt
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  5. Immanuel Schweizer
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  6. Max Mühlhäuser
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Corresponding author

Correspondence to Christian Meurisch .

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Editors and Affiliations

  1. Georg-August-Universität Göttingen, Göttingen, Germany

    Stephan Sigg

  2. University of Helsinki, Helsinki, Finland

    Petteri Nurmi

  3. RMIT University, Melbourne, VIC, Australia

    Flora Salim

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© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Meurisch, C., Hussain, T., Gogel, A., Schmidt, B., Schweizer, I., Mühlhäuser, M. (2015). A Spatiotemporal Approach for Social Situation Recognition. In: Sigg, S., Nurmi, P., Salim, F. (eds) Mobile Computing, Applications, and Services. MobiCASE 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 162. Springer, Cham. https://doi.org/10.1007/978-3-319-29003-4_18

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  • DOI: https://doi.org/10.1007/978-3-319-29003-4_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29002-7

  • Online ISBN: 978-3-319-29003-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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