Abstract
Users are increasingly interested in mobile social applications that allow them to share opinions, comments and votes about places (restaurants, shops, etc.). Among these, check-in applications are spreading rapidly. In this paper we illustrate a system able to automatically validate users’ check-in in a place exploiting device’s sensors and inferred knowledge of context (motion activity, nearby friends). Additionally, the system allows check-in suggestion to users staying in a place for a required amount of time. A description of the service scenario, of the architecture and its technical components is given, focusing on how raw context data from accelerometer onboard the device is used to recognize users’ motion situation and how it is combined with GPS position to validate check-ins.
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References
Carlotto, A., Parodi, M., Bonamico, C., Lavagetto, F., & Valla, M. (2008). Proximity classification for mobile devices using wi-fi environment similarity. In Proc. First ACM international workshop on mobile entity localization and tracking in GPS-less environments (MELT 08), September 2008, San Francisco, California, USA. doi:10.1145/1410012.1410023.
Facebook and Facebook Developer’s API. http://www.facebook.com/. http://developers.facebook.com/. Last visit 1.10.2011.
Foursquare and Foursquare Developer’s API. http://foursquare.com/. http://foursquare.com/apps/. Accessed 1.10.2011.
Gowalla and Gowalla Developer’s API. http://gowalla.com. http://gowalla.com/api/docs. Accessed 1.10.2011.
Lamorte, L., Licciardi, C. A., Marengo, M., Salmeri, A., Mohr, P., Raffa, G., Roffia, L., Pettinari, M., & Cinotti, T. S. (2007). A platform for enabling context aware telecommunication services. In Third workshop on context awareness for proactive systems, Guildford, UK.
Miluzzo, E., et al. (2008). Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application. In SenSys ’08: Proc. of the 6th ACM conference on embedded network sensor systems (pp. 337–350). New York: ACM.
Quinlan, J. R. (1992). C4.5: programs for machine learning. San Mateo: Morgan Kaufmann.
Tapia, E. M., et al. (2007). Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In Proc. of international symposium on wearable computers. New York: IEEE Press.
Tóth, N., & Pataki, B. (2008). Classification confidence weighted majority voting using decision tree classifiers. International Journal of Intelligent Computing and Cybernetics, 1(2), 169–192.
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Frà, C., Valla, M., Agneessens, A., Bisio, I., Lavagetto, F. (2012). Mobile Sensing of User’s Motion and Position Context for Automatic Check-in Suggestion and Validation. In: Lovett, T., O'Neill, E. (eds) Mobile Context Awareness. Springer, London. https://doi.org/10.1007/978-0-85729-625-2_5
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DOI: https://doi.org/10.1007/978-0-85729-625-2_5
Publisher Name: Springer, London
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