Advertisement

Incentivise Me: Smartphone-Based Mobility Detection for Pervasive Games

  • Thomas TregelEmail author
  • Felix Leber
  • Stefan Göbel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11863)

Abstract

The ubiquity of smartphones with a plethora of sensors made it possible to develop context-based pervasive games playable by most people without any additional equipment required. However, popular games like Pokémon GO or Zombie, Run! show the minimal influence context has on the gameplay besides the user’s position. Furthermore, in the context of movement and mobility the distinction between different modes of transport becomes necessary both for security and for environmental reasons.

In this paper we present a system that uses location information combined with public transport data to detect different types of mobility, especially vehicular mobility and uses this information for adaptation purposes in a prototypical location-based game. Our evaluation shows that our system can be used to reliably differentiate between public transport and driving a car within minutes and is able to adapt content in pervasive games according to the context surrounding the current user.

Keywords

Mobility Mobility detection Pervasive games Context detection 

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.
    Althoff, T., White, R.W., Horvitz, E.: Influence of Pokémon Go on physical activity: study and implications. J. Med. Internet Res. 18, e315 (2016)CrossRefGoogle Scholar
  2. 2.
    Schilit, B., Adams, N., Want, R.: Context-aware computing applications. In: Proceedings of IEEE Workshop on Mobile Computing Systems and Applications (1994)Google Scholar
  3. 3.
    Abowd, Gregory D., Dey, Anind K., Brown, Peter J., Davies, N., Smith, M., Steggles, P.: Towards a better understanding of context and context-awareness. In: Gellersen, Hans-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 304–307. Springer, Heidelberg (1999).  https://doi.org/10.1007/3-540-48157-5_29CrossRefGoogle Scholar
  4. 4.
    De Pessemier, T., Dooms, S., Martens, L.: Context-aware recommendations through context and activity recognition in a mobile environment. Multimedia Tools Appl. 72(3), 2925–2948 (2014)CrossRefGoogle Scholar
  5. 5.
    Su, X., Tong, H., Ji, P.: Activity recognition with smartphone sensors. Tsinghua Sci. Technol. 19(3), 235–249 (2014)CrossRefGoogle Scholar
  6. 6.
    Tregel, T., Gilbert, A., Konrad, R., Schäfer, P., Göbel, S.: Examining approaches for mobility detection through smartphone sensors. In: Göbel, S., et al. (eds.) JCSG 2018. LNCS, vol. 11243, pp. 217–228. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-02762-9_22CrossRefGoogle Scholar
  7. 7.
    Thiagarajan, A., Biagioni, J., Gerlich, T., Eriksson, J.: Cooperative transit tracking using smart-phones. In: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems (2010)Google Scholar
  8. 8.
    Stenneth, L., Wolfson, O., Yu, P.S., Xu, B.: Transportation mode detection using mobile phones and GIS information. In: Proceedings of the 19th International Conference on Advances in Geographic Information Systems (2011)Google Scholar
  9. 9.
    Montoya, D., Abiteboul, S., Senellart, P.: Hup-me: inferring and reconciling a timeline of user activity from rich smartphone data. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems (2015)Google Scholar
  10. 10.
    Grimes, J.G.: Global positioning system standard positioning service performance standard. Washington, D.C., U.S. Department of Defense (2008)Google Scholar
  11. 11.
    Rhein-Main-Verkehrsverbund: RMV Open Data. https://opendata.rmv.de/site/start.html. Accessed 05 Mar 2019
  12. 12.
    Google: Google Awareness API (2016). https://developers.google.com/awareness/. Accessed 15 June 2018
  13. 13.
    Tregel, T., Raymann, L., Göbel, S., Steinmetz, R.: Geodata classification for automatic content creation in location-based games. In: Alcañiz, M., Göbel, S., Ma, M., Fradinho Oliveira, M., Baalsrud Hauge, J., Marsh, T. (eds.) JCSG 2017. LNCS, vol. 10622, pp. 212–223. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-70111-0_20CrossRefGoogle Scholar
  14. 14.
    Rhein-Main-Verkehrsverbund: RMV-10-Minute-Guarantee. https://www.rmv.de/c/en/services/passenger-rights/rmv-10-minute-guarantee/. Accessed 05 Mar 2019

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.Multimedia Communications Lab – KOMTU DarmstadtDarmstadtGermany
  2. 2.TU DarmstadtDarmstadtGermany

Personalised recommendations