Learning Activity Models for Multiple Agents in a Smart Space

  • Aaron Crandall
  • Diane J. Cook


With the introduction of more complex intelligent environment systems, the possibilities for customizing system behavior have increased dramatically. Significant headway has been made in tracking individuals through spaces using wireless devices [1, 18, 26] and in recognizing activities within the space based on video data (see chapter by Brubaker et al. and [6, 8, 23]), motion sensor data [9, 25], wearable sensors [13] or other sources of information [14, 15, 22]. However, much of the theory and most of the algorithms are designed to handle one individual in the space at a time. Resident tracking, activity recognition, event prediction, and behavior automation becomes significantly more difficult for multi-agent situations, when there are multiple residents in the environment.


False Positive Rate Activity Recognition Smart Home Sensor Event Motion Sensor 
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]
    P. Bahl and V. Padmanabhan. Radar: An in-building rf-based user location and tracking system. In Proceedings of IEEE Infocom, pages 775–784, 2000.Google Scholar
  2. [2]
    O. Brdiczka, P. Reignier, and J. Crowley. Detecting individual activities from video in a smart home. In Proceedings of the International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, pages 363–370, 2007.Google Scholar
  3. [3]
    M. Colley and P. Stacey. Agent association group negotiation within intelligent environments. In Proceedings of the International Conference on Intelligent Environments, 2008.Google Scholar
  4. [4]
    D. Cook and S. K. Das. How smart are our environments? an updated look at the state of the art. Journal of Pervasive and Mobile Computing, 3(2):53–73, 2007.CrossRefGoogle Scholar
  5. [5]
    M. Diehl, M. Marsiske, A. Horgas, A. Rosenberg, J. Saczynski, and S. Willis. The revised observed activities of daily living: A performance-based assessment of everyday problem solving in older adults. Journal of Applied Gerontology, 24(3):211–230, 2005.CrossRefGoogle Scholar
  6. [6]
    W. Feng, J. Walpole, W. Feng, , and C. Pu. Moving towards massively scalable video-based sensor networks. In Proceedings of the Workshop on New Visions for Large-Scale Networks: Research and Applications, 2001.Google Scholar
  7. [7]
    J. Hightower and G. Borriello. Location systems for ubiquitous computing. IEEE Computer, 32(8):57–66, 2001.Google Scholar
  8. [8]
    S. S. Intille. Designing a home of the future. IEEE Pervasive Computing, 1:80–86, 2002.CrossRefGoogle Scholar
  9. [9]
    V. Jakkula, A. Crandall, and D. J. Cook. Knowledge discovery in entity based smart environment resident data using temporal relations based data mining. In Proceedings of the ICDM Workshop on Spatial and Spatio-Temporal Data Mining, 2007.Google Scholar
  10. [10]
    J. Krumm, S. Harris, B. Meyers, B. Brumitt, M. Hale, and S. Shafer. Multi-camera multi-person tracking for easyliving. In Proceedings of the Third IEEE International Workshop on Visual Surveillance, pages 3–10, 2000.Google Scholar
  11. [11]
    Z. Lin and L. Fu. Multi-user preference model and service provision in a smart home environment. In Proceedings of the IEEE International Conference on Automation Science and Engineering, pages 759–764, 2007.Google Scholar
  12. [12]
    C. Lu, Y. Ho, and L. Fu. Creating robust activity maps using wireless sensor network in a smart home. In Proceedings of the Third Annual Conference on Automation Science and Engineering, 2007.Google Scholar
  13. [13]
    U. Maurer, A. Smailagic, D. Siewiorek, and M. Deisher. Activity recognition and monitoring using multiple sensors on different body positions. In Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks, 2006.Google Scholar
  14. [14]
    S. Moncrieff. Multi-modal emotive computing in a smart house environment. Journal of Pervasive and Mobile Computing, special issue on Design and Use of Smart Environments (to appear), 2007.Google Scholar
  15. [15]
    R. J. Orr and G. D. Abowd. The smart floor: A mechanism for natural user identification and tracking. In Proceedings of the ACM Conference on Human Factors in Computing Systems, The Hague, Netherlands, 2000.Google Scholar
  16. [16]
    M. B. Patterson and J. L. Mack. The cleveland scale for activities of daily living (csadl): Its reliability and validity. Journal of Clinical Gerontology, 7:15–28, 2001.Google Scholar
  17. [17]
    M. Philipose, K. P. Fishkin, M. Perkowitz, D. J. Patterson, D. Hahnel, D. Fox, and H. Kautz. Inferring activities from interactions with objects. IEEE Pervasive Computing, 3(4):50–57, 2004.CrossRefGoogle Scholar
  18. [18]
    N. Priyantha, A. Chakraborty, and H. Balakrishnan. The cricket location support system. In Proceedings of the International Conference on Mobile Computing and Networking, pages pages 32–43, 2000.Google Scholar
  19. [19]
    P. Rashidi and D. Cook. Adapting to resident preferences in smart environments. In Proceedings of the AAAI Workshop on Advances in Preference Handling, 2008.Google Scholar
  20. [20]
    B. Reisberg, S. Finkel, J. Overall, N. Schmidt-Gollas, S. Kanowski, H. Lehfeld, F. Hulla, S. G. Sclan, H.-U. Wilms, K. Heininger, I. Hindmarch, M. Stemmler, L. Poon, A. Kluger, C. Cooler, M. Bergener, L. Hugonot-Diener, P. H. Robert, and H. Erzigkeit. The Alzheimer’s disease activities of daily living international scale. International Psychogeriatrics, 13:163–181, 2001.CrossRefGoogle Scholar
  21. [21]
    N. Roy, A. Roy, K. Basu, and S. K. Das. A cooperative learning framework for mobility-aware resource management in multi-inhabitant smart homes. In Proceedings of the IEEE Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous), pages 393–403, 2005.Google Scholar
  22. [22]
    D. Sanchez, M. Tentori, and J. Favela. Activity recognition for the smart hospital. IEEE Intelligent Systems, 23(2):50–57, 2008.CrossRefGoogle Scholar
  23. [23]
    L. Snidaro, C. Micheloni, and C. Chivedale. Video security for ambient intelligence. IEEE Transactions on Systems, Man and Cybernetics, Part A:Systems and Humans, 35:133–144, 2005.CrossRefGoogle Scholar
  24. [24]
    T. van Kasteren and B. Krose. Bayesian activity recognition in residence for elders. In Proceedings of the International Conference on Intelligent Environments, 2008.Google Scholar
  25. [25]
    C. Wren and E. Munguia Tapia. Toward scalable activity recognition for sensor networks. In Proceedings of the Workshop on Location and Context-Awareness, 2006.Google Scholar
  26. [26]
    J. Yin, Q. Yang, and D. Shen. Activity recognition via user-trace segmentation. ACM Transactions on Sensor Networks, 2008.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceWashington State UniversityPullmanUSA

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