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Activity Recognition in the Home Using Simple and Ubiquitous Sensors

  • Emmanuel Munguia Tapia
  • Stephen S. Intille
  • Kent Larson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3001)

Abstract

In this work, a system for recognizing activities in the home setting using a set of small and simple state-change sensors is introduced. The sensors are designed to be “tape on and forget” devices that can be quickly and ubiquitously installed in home environments. The proposed sensing system presents an alternative to sensors that are sometimes perceived as invasive, such as cameras and microphones. Unlike prior work, the system has been deployed in multiple residential environments with non-researcher occupants. Preliminary results on a small dataset show that it is possible to recognize activities of interest to medical professionals such as toileting, bathing, and grooming with detection accuracies ranging from 25% to 89% depending on the evaluation criteria used.

Keywords

Activity Recognition Sensor Activation Home Setting Experience Sampling Method Activity Label 
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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References

  1. 1.
    Elite Care’s Oatfield Estates, http://www.elite-care.com/oatfield-tech.html
  2. 2.
    Barger, T., Alwan, M., Kell, S., Turner, B., Wood, S., Naidu, A.: Objective remote assessment of activities of daily living: Analysis of meal preparation patterns. Poster presentation, Medical Automation Research Center, University of Virginia Health System (2002)Google Scholar
  3. 3.
    Clark, P., Niblett, T.: The CN2 induction algorithm. Machine Learning 3(4), 261–283 (1989)Google Scholar
  4. 4.
    Das, S.K., Cook, D.J., Bhattacharya, A., Heierman, E.O., Lin, T.Y.: The role of prediction algorithms in the MavHome smart home architecture. In: IEEE Wireless Communications, editor, vol. 9, pp. 77–84. IEEE Press, Los Alamitos (2002)Google Scholar
  5. 5.
    Domingos, P., Pazzani, M.: Beyond independence: Conditions for the optimality of a simple bayesian classifier. In: Saitta, L. (ed.) Proceedings of the Thirteenth International Conference on Machine Learning, pp. 194–202. Morgan Kaufmann, San Francisco (1996)Google Scholar
  6. 6.
    Friedman, J.: On bias, variance, 0/1 - loss, and the curse-of-dimensionality. Data mining and knowledge engineering 1, 55–77 (1997)CrossRefGoogle Scholar
  7. 7.
    Hollar, S.: COTS Dust. Ph.D. thesis, University of California, Berkeley (2001)Google Scholar
  8. 8.
    Intille, S.S., Bobick, A.F.: Recognizing planned, multi-person action. Computer Vision and Image Understanding (1077-3142) 81(3), 414–445 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Intille, S.S., Munguia Tapia, E., Rondoni, J., Beaudin, J., Kukla, C., Agarwal, S., Bao, L., Larson, K.: Tools for studying behavior and technology in natural settings. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864, pp. 157–174. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Intille, S.S., Rondoni, J., Kukla, C., Anacona, I., Bao, L.: A context-aware experience sampling tool. In: Proceedings of the Conference on Human Factors and Computing Systems: Extended Abstracts. ACM Press, New York (2003)Google Scholar
  11. 11.
    Himberg, J., Mantyjarvi, J., Seppanen, T.: Recognizing human motion with multiple acceleration sensors. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 747–752. IEEE Press, Los Alamitos (2001)Google Scholar
  12. 12.
    John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo. AAAI Press, Menlo Park (1995)Google Scholar
  13. 13.
    Kahn, J.M., Katz, R.H., Pister, K.S.J.: Mobile networking for Smart Dust. In: ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom 1999), pp. 271–278 (1999)Google Scholar
  14. 14.
    Kasten, O., Langheinrich, M.: First experiences with Bluetooth in the Smart-Its distributed sensor network. In: Workshop on Ubiquitous Computing and Communications, PACT (2001)Google Scholar
  15. 15.
    Kautz, H., Etziono, O., Fox, D., Weld, D.: Foundations of assisted cognition systems. Technical report CSE-02-AC-01, University of Washington, Department of Computer Science and Engineering (2003)Google Scholar
  16. 16.
    Langley, P., Iba, W., Thompson, K.: An analysis of Bayesian classifiers. In: Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 223–228. AAAI Press, San Jose (1992)Google Scholar
  17. 17.
    Lawton, M.P., Brody, E.M.: Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 9, 179–186 (1969)Google Scholar
  18. 18.
    Lee, S.W., Mase, K.: Activity and location recognition using wearable sensors. IEEE Pervasive Computing 1(3), 24–32 (2002)CrossRefGoogle Scholar
  19. 19.
    Makikawa, M., Iizumi, H.: Development of an ambulatory physical activity monitoring device and its application for categorization of actions in daily life. In: MEDINFO, pp. 747–750 (1995)Google Scholar
  20. 20.
    Mozer, M.: The Neural Network House: an environment that adapts to its inhabitants. In: Proceedings of the AAAI Spring Symposium on Intelligent Environments, Technical Report SS-98-02, pages 110–114, AAAI Press, Menlo Park (1998)Google Scholar
  21. 21.
    Ornstein, B.: Care technology: Smart home system for the elderly. In: Proceedings of NIST Pervasive Computing (2001)Google Scholar
  22. 22.
    Orr, R.J., Abowd, G.D.: The Smart Floor: A mechanism for natural user identification and tracking. In: Proceedings of the 2000 Conference on Human Factors in Computing Systems (CHI 2000). ACM Press, New York (2000)Google Scholar
  23. 23.
    Philipose, M., Fishkin, K.P., Fox, D., Kautz, H., Patterson, D., Perkowitz, M.: Guide: Towards understanding daily life via auto-identification and statistical analysis. In: UbiHealth Workshop, Ubicomp (2003)Google Scholar
  24. 24.
    Rogers, W.A., Meyer, B., Walker, N., Fisk, A.D.: Functional limitations to daily living tasks in the aged: a focus groups analysis. Human Factors 40, 111–125 (1998)CrossRefGoogle Scholar
  25. 25.
    Szalai. S.: The Use of Time. Daily Activities of Urban and Suburban Populations in Twelve Countries, Mouton, The Hague (1973) Edited by Alexander SzalaiGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Emmanuel Munguia Tapia
    • 1
  • Stephen S. Intille
    • 1
  • Kent Larson
    • 1
  1. 1.Massachusetts Institute of TechnologyCambridgeUSA

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