Farther Than You May Think: An Empirical Investigation of the Proximity of Users to Their Mobile Phones

  • Shwetak N. Patel
  • Julie A. Kientz
  • Gillian R. Hayes
  • Sooraj Bhat
  • Gregory D. Abowd
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4206)


Implicit in much research and application development for mobile phones is the assumption that the mobile phone is a suitable proxy for its owner’s location. We report an in-depth empirical investigation of this assumption in which we measured proximity of the phone to its owner over several weeks of continual observation. Our findings, summarizing results over 16 different subjects of a variety of ages and occupations, establish baseline statistics for the proximity relationship in a typical US metropolitan market. Supplemental interviews help us to establish reasons why the phone and owner are separated, leading to guidelines for developing mobile phone applications that can be smart with respect to the proximity assumption. We show it is possible to predict the proximity relationship with 86% confidence using simple parameters of the phone, such as current cell ID, current date and time, signal status, charger status and ring/vibrate mode.


Mobile Phone Ubiquitous Computing Mobile Phone User Experience Sampling Method Proximity Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Cohen, J.: A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20, 37–48 (1960)CrossRefGoogle Scholar
  2. 2.
    Consolvo, S., Walker, M.: Using the experience sampling method to evalute ubicomp applications. In: IEEE Pervasive Computing, pp. 24–31 (2003)Google Scholar
  3. 3.
    Corner, M.D., Noble, B.D.: Zero-Interaction Authentication. In: Mobicom 2002. ACM, Atlanta (2002)Google Scholar
  4. 4.
    Davis, M., Good, N., Sarvas, R.: From Context to Content: Leveraging Context for Mobile Media Metadata. In: International Multimedia Conference: 12th annual ACM international conference on Multimedia, New York, NY, USA (2004)Google Scholar
  5. 5.
    Demumieux, R., Losquin, P.: Gather customer’s real usage on mobile phones. In: Conference on Human Computer Interaction with Mobile Devices & Services (2005)Google Scholar
  6. 6.
    Eagle, N., Pentland, A.: Reality Mining: Sensing Complex Social Systems. Personal and Ubiquitous Computing 10(4), 255–268 (2005)CrossRefGoogle Scholar
  7. 7.
    Hayes, G.R., Patel, S.N., Truong, K.N., Iachello, G., Kientz, J.A., Farmer, R., Abowd, G.D.: The personal audio loop: Designing a ubiquitous audio-based memory aid. In: Dunlop, M.D. (ed.) Mobile HCI 2004. LNCS, vol. 3160, pp. 168–179. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Helal, S., Giraldo, C., Kaddoura, Y., Lee, C., Zabadani, H.e., Mann, W.: Smart phone based cognitive assistant. In: UbiHealth 2003: The 2nd International Workshop on Ubiquitous Computing for Pervasive Healthcare Applications, Seattle, Washington (2003)Google Scholar
  9. 9.
    Hightower, J., Borriello, G.: A Survey and Taxonomy of Location Systems for Ubiquitous Computing, University of Washington (2001)Google Scholar
  10. 10.
    Kahneman, D., Krueger, A.B., Schkade, D.A., Schwarz, N., Stone, A.A.: A Survey Method for Characterizing Daily Life Experience: The Day Reconstruction Method. Science, 1776–1780 (2004)Google Scholar
  11. 11.
    Laasonen, K.: Clustering and Prediction of Mobile User Routes from Cellular Data. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 569–576. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    LaMarca, A., et al.: 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
  13. 13.
    Lamming, M.: SPECs: Another approach to human context and activity sensing research, using tiny peer-to-peer wireless computers. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864, pp. 192–199. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  14. 14.
    McGuigan, J.: Towards a Sociology of the Mobile Phone. Human Technology (2005)Google Scholar
  15. 15.
    Mitchell, T.: Machine Learning. McGraw-Hill, New York (1996)zbMATHGoogle Scholar
  16. 16.
    Otsason, V., Varshavsky, A., LaMarca, A., de Lara, E.: Accurate GSM indoor localization. In: Beigl, M., Intille, S.S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005. LNCS, vol. 3660, pp. 141–158. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Oulasvirta, A., Raento, M., Tiitta, S.: ContextContacts: Re-Designing SmartPhone’s Contact Book to Support Mobile Awareness and Collaboration. In: MobileHCI 2005 (2005)Google Scholar
  18. 18.
    Palen, L., Salzman, M.: Beyond the handset: designing for wireless communications usability. ACM Transactions on Computer-Human Interaction 9(2), 125–151 (2002)CrossRefGoogle Scholar
  19. 19.
    Palen, L., Salzman, M., Youngs, E.: Going Wireless: Behavior & Practice of New Mobile Phone Users. In: CSCW 2000: Computer Supported Cooperative Work (2000)Google Scholar
  20. 20.
    Patel, S.N., Pierce, J.S., Abowd, G.D.: A gesture-based authentication scheme for untrusted public terminals. In: 17th annual ACM symposium on User interface software and technology 2004. ACM Press, Santa Fe (2004)Google Scholar
  21. 21.
    Preece, J., Rogers, Y., Sharp, H.: Interaction design: beyond human-computer interaction. John Wiley & Sons, Inc., Chichester (2002)Google Scholar
  22. 22.
    Raento, M., Oulasvirta, A., Petit, R., Toivonen, H.: ContextPhone - A prototyping platform for context-aware mobile applications. In: IEEE Pervasive Computing (2005)Google Scholar
  23. 23.
    Satyanarayanan, M.: Swiss Army Knife or Wallet? In: IEEE Pervasive Computing (2005)Google Scholar
  24. 24.
    Schlosser, F.K.: So, how do people really use their handheld devices? An interactive study of wireless technology use. Journal of Organizational Behavior 23(4), 401–423 (2002)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Sullivan, J., Fischer, G.: Mobile Architectures and Prototypes to Assist Persons with Cognitive Disabilities using Public Transportation. In: 26th International Conference on Technology and Rehabilitation, Atlanta GA, USA (2003)Google Scholar
  26. 26.
    Vaananen-Vainio-Mattila, K., Ruuska, S.: User Needs for Mobile Communication Devices: Requirements Gathering and Analysis through Contextual Inquiry. In: First Workshop on HCI and Mobile Devices (1998)Google Scholar
  27. 27.
    Wang, X.H., Istepanian, R.S.H., Song, Y.H.: Mobile e-Health: The Unwired Evolution of Telemedicine. Telemedicine Journal and e-Health (2003)Google Scholar
  28. 28.
    Want, R., Pering, T., Danneels, G., Kumar, M., Sundar, M., Light, J.: The Personal Server: Changing the Way We Think about Ubiquitous Computing. In: Borriello, G., Holmquist, L.E. (eds.) UbiComp 2002. LNCS, vol. 2498, p. 194. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shwetak N. Patel
    • 1
  • Julie A. Kientz
    • 1
  • Gillian R. Hayes
    • 1
  • Sooraj Bhat
    • 1
  • Gregory D. Abowd
    • 1
  1. 1.College of Computing & GVU CenterGeorgia Institute of TechnologyAtlantaUSA

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