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Smart Device Stealing and CANDIES

  • Martin JänickeEmail author
  • Viktor Schmidt
  • Bernhard Sick
  • Sven Tomforde
  • Paul Lukowicz
  • Jörn Schmeißing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11352)

Abstract

Personal devices such as smart phones are increasingly utilized in everyday life. Frequently, activity recognition is performed on these devices to estimate the current user status and trigger automated actions according to the user’s needs. In this article, we focus on improving the self-awareness of such systems in terms of detecting theft: We equip devices with the capabilities to model their own user and to, e.g., alarm the legal owner if an unexpected other person is carrying the device. We gathered 24 h of data in a case study with 14 persons using a Nokia N97 and trained an activity recognition system. Using the data from this study, we investigated several autonomous novelty detection techniques, that ultimately led to the development of CANDIES. The algorithm is able to continuously check if the observed user behavior corresponds to the initial model, triggering an alarm if not. Our evaluations show that the presented methods are highly successful with a theft detection rate of over 85% for the trained set of persons. Comparing the experiments with state of the art techniques support the strong practicality of our approach.

Keywords

Smart devices Gaussian mixture model Organic computing Self-awareness CANDIES Probabilistic theft detection 

Notes

Acknowledgements

The authors would like to thank the German research foundation (Deutsche Forschungsgemeinschaft, DFG) for the financial support in the context of the “Organic Computing Techniques for Runtime Self-Adaptation of Multi-Modal Activity Recognition Systems” project (SI 674/12-1, LU 1574/2-1).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Martin Jänicke
    • 1
    Email author
  • Viktor Schmidt
    • 1
  • Bernhard Sick
    • 1
  • Sven Tomforde
    • 1
  • Paul Lukowicz
    • 2
  • Jörn Schmeißing
    • 3
  1. 1.Intelligent Embedded SystemsUniversity of KasselKasselGermany
  2. 2.German Research Center for Artificial IntelligenceKaiserslauternGermany
  3. 3.University of KasselKasselGermany

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