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A Machine Learning Approach for Walker Identification Using Smartphone Sensors

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Complex Pattern Mining

Part of the book series: Studies in Computational Intelligence ((SCI,volume 880))

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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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Notes

  1. 1.

    http://www.cs.waikato.ac.nz/ml/weka/.

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Author information

Authors and Affiliations

  1. Giustino Fortunato University, Benevento, Italy

    Antonio Angrisano & Mario Luca Bernardi

  2. University of Bari Aldo Moro, Bari, Italy

    Pasquale Ardimento

  3. Unitelma Sapienza University, Rome, Italy

    Marta Cimitile

  4. Parthenope University, Naples, Italy

    Salvatore Gaglione

Authors
  1. Antonio Angrisano
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  2. Pasquale Ardimento
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  3. Mario Luca Bernardi
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  4. Marta Cimitile
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  5. Salvatore Gaglione
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Corresponding author

Correspondence to Pasquale Ardimento .

Editor information

Editors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  2. Dipartimento di Informatica, Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  3. Dipartimento di Informatica, Università degli Studi di Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  4. ICAR-CNR, Rende, Italy

    Giuseppe Manco

  5. Università degli Studi di Napoli Federico II, Naples, Italy

    Elio Masciari

  6. Department of Computer Science, University of North Carolina, Charlotte, NC, USA

    Zbigniew W. Ras

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Angrisano, A., Ardimento, P., Bernardi, M.L., Cimitile, M., Gaglione, S. (2020). A Machine Learning Approach for Walker Identification Using Smartphone Sensors. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) Complex Pattern Mining. Studies in Computational Intelligence, vol 880. Springer, Cham. https://doi.org/10.1007/978-3-030-36617-9_14

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