Augmenting Mobile Localization with Activities and Common Sense Knowledge

  • Nicola Bicocchi
  • Gabriella Castelli
  • Marco Mamei
  • Franco Zambonelli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7040)

Abstract

Location is a key element for ambient intelligence services. Due to GPS inaccuracies, inferring high level information (i.e., being at home, at work, in a restaurant) from geographic coordinates in still non trivial. In this paper we use information about activities being performed by the user to improve location recognition accuracy. Unlike traditional methods, relations between locations and activities are not extracted from training data but from an external commonsense knowledge base. Our approach maps location and activity labels to concepts organized within the ConceptNet network. Then, it verifies their commonsense proximity by implementing a bio-inspired greedy algorithm. Experimental results show a sharp increase in localization accuracy.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Azizyan, M., Choudhury, R.R.: Surroundsense: mobile phone localization using ambient sound and light. SIGMOBILE Mob. Comput. Commun. Rev. 13, 69–72 (2009)CrossRefGoogle Scholar
  2. 2.
    Azizyan, M., Constandache, I., Roy Choudhury, R.: Surroundsense: mobile phone localization via ambience fingerprinting. In: Proceedings of the 15th Annual International Conference on Mobile Computing and Networking, MobiCom 2009, pp. 261–272. ACM, New York (2009)Google Scholar
  3. 3.
    Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data, pp. 1–17. Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Bicocchi, N., Castelli, G., Mamei, M., Rosi, A., Zambonelli, F.: Supporting location-aware services for mobile users with the whereabouts diary. In: Proceedings of the 1st International Conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications, MOBILWARE 2008, pp. 6:1–6:6. ICST, Brussels (2008)Google Scholar
  5. 5.
    Bicocchi, N., Mamei, M., Zambonelli, F.: Detecting activities from body-worn accelerometers via instance-based algorithms. Pervasive and Mobile Computing 6(4), 482–495 (2010)CrossRefGoogle Scholar
  6. 6.
    Brush, A.B., Karlson, A.K., Scott, J., Sarin, R., Jacobs, A., Bond, B., Murillo, O., Hunt, G., Sinclair, M., Hammil, K., Levi, S.: User experiences with activity-based navigation on mobile devices. In: Proceedings of the 12th International Conference on Human Computer Interaction with Mobile Devices and Services, MobileHCI 2010, pp. 73–82. ACM, New York (2010)Google Scholar
  7. 7.
    Chung Cheng, Y., Chawathe, Y., Lamarca, A., Krumm, J.: Accuracy characterization for metropolitan-scale wi-fi localization. In: Proceedings of Mobisys 2005, pp. 233–245 (2005)Google Scholar
  8. 8.
    Duong, T., Phung, D., Bui, H., Venkatesh, S.: Efficient duration and hierarchical modeling for human activity recognition. Artificial Intelligence 173, 830–856 (2009)CrossRefGoogle Scholar
  9. 9.
    Ferrari, L., Mamei, M.: Discovering daily routines from google latitude with topic models. In: IEEE International Conference on Pervasive Computing and Communications, Workshop on Context Modeling and Reasoning. IEEE Computer Society, Washington, DC, USA (2011)Google Scholar
  10. 10.
    Hightower, J.: From position to place. In: Proceedings of The 2003 Workshop on Location-Aware Computing, pp. 10–12 (October 2003)Google Scholar
  11. 11.
    Jung, D., Teixeira, T., Savvides, A.: Towards cooperative localization of wearable sensors using accelerometers and cameras. In: Proceedings of the 29th Conference on Information Communications, INFOCOM 2010, pp. 2330–2338. IEEE Press, Piscataway (2010)Google Scholar
  12. 12.
    Krumm, J.: Ubiquitous Advertising: The Killer Application for the 21st Century. IEEE Pervasive Computing 10(1), 66–73 (2011)CrossRefGoogle Scholar
  13. 13.
    Liao, L., Fox, D., Kautz, H.: Extracting places and activities from gps traces using hierarchical conditional random fields. Int. J. Rob. Res. 26, 119–134 (2007)CrossRefGoogle Scholar
  14. 14.
    Majewski, P., Szymański, J.: Text Categorization with Semantic Commonsense Knowledge: First Results. In: Neural Information Processing, pp. 769–778. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Mamei, M.: Applying commonsense reasoning to place identification. IJHCR 1(2), 36–53 (2010)Google Scholar
  16. 16.
    Ofstad, A., Nicholas, E., Szcodronski, R., Choudhury, R.R.: Aampl: accelerometer augmented mobile phone localization. In: Proceedings of the first ACM International Workshop on Mobile Entity Localization and Tracking in GPS-Less Environments, MELT 2008, pp. 13–18. ACM, New York (2008)CrossRefGoogle Scholar
  17. 17.
    LaMarca, A., Chawathe, Y., Consolvo, S., Hightower, J., Smith, I., Scott, J., Sohn, T., Howard, J., Hughes, J., Potter, F., Tabert, J., Powledge, P., Borriello, G., Schilit, B.: Place Lab: Device Positioning Using Radio Beacons in the Wild. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 116–133. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Zheng, Y., Zhang, L., Ma, Z., Xie, X., Ma, W.-Y.: Recommending friends and locations based on individual location history. ACM Trans. Web 5, 5:1–5:44 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nicola Bicocchi
    • 1
  • Gabriella Castelli
    • 2
  • Marco Mamei
    • 2
  • Franco Zambonelli
    • 2
  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversitá di Modena e Reggio EmiliaItaly
  2. 2.Dipartimento di Scienze e Metodi dell’IngegneriaUniversitá di Modena e Reggio EmiliaItaly

Personalised recommendations