ABSTRACT
Much research in ubiquitous computing assumes that a user's phone will be always on and at-hand, for collecting user context and for communicating with a user. Previous work with the previous generation of mobile phones has shown that such an assumption is false. Here, we investigate whether this assumption about users' proximity to their mobile phones holds for a new generation of mobile phones, smart phones. We conduct a data collection field study of 28 smart phone owners over a period of 4 weeks. We show that in fact this assumption is still false, with the within arm's reach proximity being true close to 50% of the time, similar to the earlier work. However, we also show that smart phone proximity within the same room (arm+room) as the user is true almost 90% of the time. We discuss the reasons for these phone proximities and the implications of this on the development of mobile phone applications, particularly those that collect user and environmental context, and delivering notification to users. We also show that we can accurately predict the proximity at the arm level and arm+room level with 75 and 83% accuracy, respectively, with features simple to collect and model on a mobile phone. Further we show that for several individuals who are almost always within the arm+room level, we can predict this level with over 90% accuracy.
Supplemental Material
- Banjo, O., Hu, Y. and Sundar, S.S. Cell phone usage and social interaction with proximate others: Ringing in theoretical model. The Open Communication Journal, 2, 2008: 127--135.Google ScholarCross Ref
- Brandt, J., Weiss, N., and Klemmer, S. R. Lowering the burden for diary studies under mobile conditions. CHI '07 extended abstracts. 2007. 2303--2308. Google ScholarDigital Library
- Consolvo, S., et al. Activity sensing in the wild: a field trial of ubifit garden. CHI '08. 2008. 1797--1806. Google ScholarDigital Library
- Cozza, R. Forecast: Mobile Communications Devices by Open Operating System, Worldwide. Gartner Report. 2011.Google Scholar
- Cozza, R., et al. A. Market Share Analysis: Mobile Devices, Worldwide, 4Q10 and 2010. Gartner Report. 2011.Google Scholar
- Eagle, N. and Pentland, A.S. Reality mining: sensing complex social systems. Personal Ubiquitous Computing, 10 (March 2006): 255--268. Google ScholarDigital Library
- Entner, R. Smartphones to Overtake Feature Phones in U.S. by 2011, Nielsen Wire, http://blog.nielsen.com/nielsenwire/consumer/smartphones-to-overtake-feature-phones-in-u-s-by-2011/, March 26, 2010.Google Scholar
- Garzonis, S. Mobile Service Awareness Via Auditory Notifications. Doctoral thesis, University of Bath, June 2010.Google Scholar
- Györb'1ró, N., Fábián, Á. and Hományi, G. An activity recognition system for mobile phones. Mobile Networks and Applications, 14 (February 2009): 82--91. Google ScholarDigital Library
- Hall, M., et al. The WEKA Data Mining Software: An Update. SIGKDD Explorations, 11(1), 2009: 10--1. Google ScholarDigital Library
- Helal, S., et al. Smart phone based cognitive assistant. UbiHealth 2003, Workshop on Ubiquitous Computing for Pervasive Healthcare Applications. 2003.Google Scholar
- Henderson-Summet, V. Facilitating Communication for Deaf Individuals with Mobile Technologies. Doctoral thesis, Georgia Institute of Technology, 2010.Google Scholar
- Kahneman, D., et al. A Survey Method for Characterizing Daily Life Experience: The Day Reconstruction Method. Science, 306 (December 2004): 1776--1780.Google Scholar
- Kellogg, D. Among Mobile Phone Ussrs, Hispanics, Asians are Most-Likely Smartphone Owners in the U.S. Nielsen Wire, http://blog.nielsen.com/nielsenwire/consumer/among-mobile-phone-users-hispanics-asians-are-most-likely-smartphone-owners-in-the-u-s, February 2, 2011.Google Scholar
- Krause, A., Smailagic, A., Siewiorek, D.P. Context-Aware Mobile Computing: Learning Context-Dependent Personal Preferences from Wearable Sensor Array. IEEE Transactions on Mobile Computing, 5(2) Feb. 2006 pp. 113--12. Google ScholarDigital Library
- Krumm, J. Ubiquitous Advertising: The Killer Application for the 21st Century. IEEE Pervasive Computing, 10 (January 2011): 66--73. Google ScholarDigital Library
- Laasonen, K. Clustering and prediction of mobile user routes from cellular data, 2005. Knowledge Discovery in Databases. 2005. 569--576. Google ScholarDigital Library
- Massimi, M., Baecker, R.M., and Wu, M. Using participatory activities with seniors to critique, build, and evaluate mobile phones. ASSETS 2007. 2007, 155--162. Google ScholarDigital Library
- Partridge, K. and Golle, P. On using existing time-use study data for ubiquitous computing applications. In UbiComp'08, 2008, 144--153. Google ScholarDigital Library
- Patel, S.N., et al. Farther Than You May Think: An Empirical Investigation of the Proximity of Users to Their Mobile Phones. UbiComp '06. 2006, 123--140. Google ScholarDigital Library
- Preuveneers, D. and Berbers, Y. Mobile phones assisting with health self-care: a diabetes case study. MobileHCI '08. 2008, 177--186. Google ScholarDigital Library
- Satyanarayanan, M. Swiss army knife or wallet? IEEE Pervasive 4(2), Jan.-Mar., 2005, 2--3. Google ScholarDigital Library
- Varshavsky, A. and Patel, S. Location in Ubiquitous Computing. In Krumm, J. (ed.), Ubiquitous Computing Fundamentals, Taylor and Francis Group, Florida, (2010) Chapter 8, 285--319.Google Scholar
Index Terms
- Getting closer: an empirical investigation of the proximity of user to their smart phones
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