A Quantitative Method for Revealing and Comparing Places in the Home

  • Ryan Aipperspach
  • Tye Rattenbury
  • Allison Woodruff
  • John Canny
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4206)


Increasing availability of sensor-based location traces for individuals, combined with the goal of better understanding user context, has resulted in a recent emphasis on algorithms for automatically extracting users’ significant places from location data. Place-finding can be characterized by two sub-problems, (1) finding significant locations, and (2) assigning semantic labels to those locations (the problem of “moving from location to place”) [8]. Existing algorithms focus on the first sub-problem and on finding city-level locations. We use a principled approach in adapting Gaussian Mixture Models (GMMs) to provide a first solution for finding significant places within the home, based on the first set of long-term, precise location data collected from several homes. We also present a novel metric for quantifying the similarity between places, which has the potential to assign semantic labels to places by comparing them to a library of known places. We discuss several implications of these new techniques for the design of Ubicomp systems.


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  1. 1.
    Aipperspach, R., Cohen, E., Canny, J.: Analysis and Prediction of Sensor Data Collected from Smart Homes. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Aipperspach, R., Woodruff, A., Anderson, K., Hooker, B.: Maps of Our Lives: Sensing People and Objects Together in the Home. Tech. Report EECS-2005-22, EECS Department, UC Berkeley (2005)Google Scholar
  3. 3.
    Ashbrook, D., Starner, T.: Using GPS to Learn Significant Locations and Predict Movement Across Multiple Users. Personal and Ubiquitous Comp. 7 (2003)Google Scholar
  4. 4.
    Dey, A., et al.: A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications. Human-Computer Interaction 16 (2001)Google Scholar
  5. 5.
    Dourish, P.: What We Talk About When We Talk About Context. Personal and Ubiquitous Computing 8, 19–30 (2004)CrossRefGoogle Scholar
  6. 6.
    Dragunov, A.N., et al.: TaskTracer: A Desktop Environment to Support Multi-tasking Knowledge Workers. In: Int’l. Conf. on Intelligent User Interfaces (2005)Google Scholar
  7. 7.
    Harrison, S., Dourish, P.: Re-Place-ing Space: The Roles of Place and Space in Collaborative Systems. In: Proc. CSCW 1996 (1996)Google Scholar
  8. 8.
    Hightower, J., et al.: Learning and recognizing the places we go. In: Beigl, M., Intille, S.S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005. LNCS, vol. 3660, pp. 159–176. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Kang, J.H., Welbourne, W., Stewart, B., Borriello, G.: Extracting Places from Traces of Locations. In: Proc. Workshop on Wireless Mobile Applications and Services on WLAN Hotspots, WMASH (2004)Google Scholar
  10. 10.
    Laasonen, K., Raento, M., Toivonen, H.: Adaptive on-device location recognition. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 287–304. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Leont’ev, A.N.: Activity, Consciousness, and Personality. Prentice-Hall, Englewood Cliffs (1978)Google Scholar
  12. 12.
    Liao, L., Fox, D., Kautz, H.: Learning and Inferring Transportation Routines. In: Proc. AAAI 2004 (2004)Google Scholar
  13. 13.
    Marmasse, N., Schmandt, C.: Location-aware information delivery with comMotion. In: Thomas, P., Gellersen, H.-W. (eds.) HUC 2000. LNCS, vol. 1927, p. 157. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  14. 14.
    Mehta, N., Natarajan, S., Tadepalli, P., Fern, A.: Transfer in Variable-Reward Hierarchical Reinforcement Learning. In: Proc. of the Inductive Transfer workshop at NIPS 2005 (2005)Google Scholar
  15. 15.
    Monteiro, C.: Activity Analysis in Houses of Recife, Brazil. In: Proc. First Int’l. Space Syntax Conf., pp. 20.1–20.13 (1997)Google Scholar
  16. 16.
    Morris, M., Intille, S.S., Beaudin, J.S.: Embedded assessment: Overcoming barriers to early detection with pervasive computing. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 333–346. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Morris, M.: Social Networks as Health Feedback Displays. IEEE Internet Computing 9(5), 29–37 (2005)CrossRefGoogle Scholar
  18. 18.
    Oswald, F., et al.: The Role of the Home Environment in Middle and Late Adulthood. In: Wahl, H.-W., et al. (eds.) The Many Faces of Health, Competence and Well-being in Old Age: Integrating Epidemiological, Psychological and Social Perspectives. Springer, Heidelberg (2006)Google Scholar
  19. 19.
    Philipose, M., et al.: Inferring Activities from Interactions with Objects. IEEE Pervasive Computing, pp. 50–57 (October 2004)Google Scholar
  20. 20.
    Rowan, J., Mynatt, E.D.: Digital Family Portraits: Providing Peace of Mind for Extended Family Members. In: Proc. CHI 2001 (2001)Google Scholar
  21. 21.
    Stolcke, A.: SRILM - An Extensible Language Modeling Toolkit. In: Proc. of the Int’l. Conf. on Spoken Language Processing (2002)Google Scholar
  22. 22.
    Tapia, E.M., Intille, S.S., Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  23. 23.
    Trevisani, E., Vitaletti, A.: Cell-Id Location Technique, Limits and Benefits: An Experimental Study. In: Proc. IEEE Workshop on Mobile Computing Systems and Applications, WMCSA (2004)Google Scholar
  24. 24.
    Truong, E., et al.: CAMP: A magnetic poetry interface for end-user programming of capture applications for the home. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, pp. 143–160. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  25. 25.
    Ubisense, Ltd. Website, http://www.ubisense.net
  26. 26.
    Wang, J., Canny, J.: End-User Place Annotation on Mobile Devices: A Comparative Study. In: Extended Abstracts, CHI 2006 (2006)Google Scholar
  27. 27.
    Wicker, A.: An Introduction to Ecological Psychology. Brooks/Cole Publishing Company, Monterey (1979)Google Scholar
  28. 28.
    Woodruff, A., Anderson, A., Mainwaring, S.D., Aipperspach, R.: Portable, But Not Mobile: A Study of Wireless Laptops in the Home. Tech. Report EECS-2006-88, EECS Department, UC Berkeley (2006)Google Scholar
  29. 29.
    Wyatt, D., Philipose, M., Choudhury, T.: Unsupervised Activity Recognition Using Automatically Mined Common Sense. In: Proc. of AAAI 2005 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ryan Aipperspach
    • 1
    • 2
  • Tye Rattenbury
    • 1
  • Allison Woodruff
    • 2
  • John Canny
    • 1
  1. 1.Berkeley Institute of Design, Computer Science DivisionUniversity of CaliforniaBerkeleyUSA
  2. 2.Intel ResearchBerkeleyUSA

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