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Personal and Ubiquitous Computing

, Volume 17, Issue 3, pp 413–431 | Cite as

Human interaction discovery in smartphone proximity networks

  • Trinh Minh Tri Do
  • Daniel Gatica-Perez
Original Article

Abstract

Since humans are fundamentally social beings and interact frequently with others in their daily life, understanding social context is of primary importance in building context-aware applications. In this paper, using smartphone Bluetooth as a proximity sensor to create social networks, we present a probabilistic approach to mine human interaction types in real life. Our analysis is conducted on Bluetooth data continuously sensed with smartphones for over one year from 40 individuals who are professionally or personally related. The results show that the model can automatically discover a variety of social contexts. We objectively validated our model by studying its predictive and retrieval performance.

Keywords

Social Network Analysis Time Slice Interaction Type Retrieval Performance Temporal Context 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was funded by Nokia Research Center Lausanne (NRC) through the LS-CONTEXT project.

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Idiap Research InstituteMartignySwitzerland
  2. 2.École Polytechnique Fédérale de LausanneLausanneSwitzerland

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