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Examining Approaches for Mobility Detection Through Smartphone Sensors

  • Thomas TregelEmail author
  • Andreas Gilbert
  • Robert Konrad
  • Petra Schäfer
  • Stefan Göbel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11243)

Abstract

The ubiquity of smartphones with integrated positioning systems, and multiple sensors for movement detection made it possible to develop context-sensitive applications for both productivity and entertainment. Location-based games like Ingress or Pokémon Go have demonstrated the public interest in this genre of mobile-only games – games that are exclusively available for mobile devices due to their sensor integration. For these games mobility is a key component, which defines and influences the game’s flow directly.

In this paper we compare different approaches and available frameworks for mobility detection and examine the frameworks’ performances in a scenario-based evaluation.

Based on our finding we present our own approach to differentiate between different modes of public transport and other common modes of movement like walking, running or riding a bicycle. Our approach already reaches an accuracy of 87% with a small sample size.

Keywords

Mobility Mobility detection Machine learning 

Notes

Acknowledgment

The research presented in this paper was partially funded by the LOEWE initiative (Hessen, Germany) within the research project “Infrastruktur – Design – Gesellschaft” as project mo.de.

References

  1. 1.
    Schonfeld, E.: Mobile OS 2009 market share (2017). https://techcrunch.com/2010/02/23/smartphone-iphone-sales-2009-gartner/. Accessed 15 June 2018
  2. 2.
    Lau, S.L., David, K.: Movement recognition using the accelerometer in smartphones. In: Future Network and Mobile Summit (2010)Google Scholar
  3. 3.
    Wirtl, T., Nickel, C.: Aktivitätserkennung auf Smartphones. In: International Conference of the Biometrics Special Interest Group (2011)Google Scholar
  4. 4.
    Hemminki, S., Nurmi, P., Tarkoma, S.: Accelerometer-based transportation mode detection on smartphones. In: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (2013)Google Scholar
  5. 5.
    Anjum, A., Ilyas, M.U.: Activity recognition using smartphone sensors. In: IEEE 10th Consumer Communications and Networking Conference (2013)Google Scholar
  6. 6.
    Google: Google Awareness API (2016). https://developers.google.com/awareness/. Accessed 15 June 2018
  7. 7.
    Neura: Neura SDK (2017). https://dev.theneura.com/. Accessed 15 June 2018
  8. 8.
    Holmes, G., Donkin, A., Witten, I.H.: Weka: a machine learning workbench. In: Proceedings of the 1994 Second Australian and New Zealand Conference on Intelligent Information Systems (1994)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Thomas Tregel
    • 1
    Email author
  • Andreas Gilbert
    • 2
  • Robert Konrad
    • 1
  • Petra Schäfer
    • 2
  • Stefan Göbel
    • 1
  1. 1.TU DarmstadtDarmstadtGermany
  2. 2.Frankfurt University of Applied SciencesFrankfurtGermany

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