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A Machine Learning Approach for Walker Identification Using Smartphone Sensors

  • Antonio Angrisano
  • Pasquale ArdimentoEmail author
  • Mario Luca Bernardi
  • Marta Cimitile
  • Salvatore Gaglione
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 880)

Abstract

Nowadays, smartphones are equipped with MEMS sensors like accelerometers, gyroscopes, and magnetometers. In this work we exploited this kind of sensors to provide advanced information about the walker bringing the smartphone. In particular, smartphone sensors outputs are used to recognize the identity of the walker and the pose of the device during the walk. If the aforementioned information was known, it could be used to improve the functionalities of specific smartphones. For instance, the recognition of walker identity can be used for theft protection or the device pose can be used to improve the performance of the pedestrian navigation. In this paper, we adopted a decision tree classifier approach to recognize the previously described contexts using data produced by smartphone sensors, obtaining effective results.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Antonio Angrisano
    • 1
  • Pasquale Ardimento
    • 2
    Email author
  • Mario Luca Bernardi
    • 1
  • Marta Cimitile
    • 3
  • Salvatore Gaglione
    • 4
  1. 1.Giustino Fortunato UniversityBeneventoItaly
  2. 2.University of Bari Aldo MoroBariItaly
  3. 3.Unitelma Sapienza UniversityRomeItaly
  4. 4.Parthenope UniversityNaplesItaly

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