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From On-Going to Complete Activity Recognition Exploiting Related Activities

  • Carlo Nicolini
  • Bruno Lepri
  • Stefano Teso
  • Andrea Passerini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6219)

Abstract

Activity recognition can be seen as a local task aimed at identifying an on-going activity performed at a certain time, or a global one identifying time segments in which a certain activity is being performed. We combine these tasks by a hierarchical approach which locally predicts on-going activities by a Support Vector Machine and globally refines them by a Conditional Random Field focused on time segments involving related activities. By varying temporal scales in order to account for widely different activity durations, we achieve substantial improvements in on-going activity recognition on a realistic dataset from the PlaceLab sensing environment. When focusing on periods within which related activities are known to be performed, the refinement stage manages to exploit these relationships in order to correct inaccurate local predictions.

Keywords

Support Vector Machine Activity Recognition Conditional Random Field Local Prediction Inductive Logic Programming 
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.
    Katz, S.: Assessing self-maintenance: Activities of daily living, mobility, and instrumental activities of daily living. Journal of American Geriatrics Society 31(12), 712–726 (1983)CrossRefGoogle Scholar
  2. 2.
    Lepri, B., Mana, N., Cappelletti, A., Pianesi, F., Zancanaro, M.: What is happening now? detection of activities of daily living from simple visual features. Personal and Ubiquitous Computing (2010)Google Scholar
  3. 3.
    Philipose, M.P.K., Perkowitz, M., Patterson, D.J., Fox, D., Kautz, H., Hähnel, D.: Inferring activities from interactions with objects. IEEE Pervasive Computing 3, 50–57 (2004)CrossRefGoogle Scholar
  4. 4.
    Pentney, W., Philipose, M., Bilmes, J.A., Kautz, H.A.: Learning large scale common sense models of everyday life. In: AAAI, pp. 465–470 (2007)Google Scholar
  5. 5.
    Logan, B., Healey, J., Philipose, M., Tapia, E.M., Intille, S.: A long-term evaluation of sensing modalities for activity recognition. In: Krumm, J., Abowd, G.D., Seneviratne, A., Strang, T. (eds.) UbiComp 2007. LNCS, vol. 4717, pp. 483–500. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Stikic, M., Huynh, T., Van Laerhoven, K., Schiele, B.: Adl recognition based on the combination of rfid and accelerometer sensing. In: 2nd International Conference on Pervasive Computing Technologies for Healthcare 2008 (2008)Google Scholar
  7. 7.
    Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Lester, J., Choudhury, T., Borriello, G.: A practical approach to recognizing physical activities. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 1–16. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    Wyatt, D., Philipose, M., Choudhury, T.: Unsupervised activity recognition using automatically mined common sense. In: AAAI, pp. 21–27 (2005)Google Scholar
  11. 11.
    Oliver, N., Horvitz, E., Garg, A.: Layered representations for human activity recognition. In: Fourth IEEE Int. Conf. on Multimodal Interfaces, pp. 3–8 (2002)Google Scholar
  12. 12.
    Ogawa, M., Ochiai, S., Shoji, K., Nishihara, M., Togawa, T.: An attempt of monitoring daily activities at home. In: 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, pp. 786–788 (2000)Google Scholar
  13. 13.
    Blanke, U., Schiele, B.: Scalable recognition of daily activities with wearable sensors. In: Hightower, J., Schiele, B., Strang, T. (eds.) LoCA 2007. LNCS, vol. 4718, pp. 50–67. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In: UbiComp 2008, pp. 1–9. ACM, New York (2008)Google Scholar
  15. 15.
    Liao, L., Fox, D., Kautz, H.: Location-based activity recognition using relational markov networks. In: IJCAI 2005 (2005)Google Scholar
  16. 16.
    Landwher, N., Gutmann, B., Thon, I., Philipose, M., De Raedt, L.: Relational transformation-based tagging for human activity recognition. In: Proceedings of the 6th Workshop on Multi-Relational Data Mining (MRDM), Warsaw, Poland (September 2007)Google Scholar
  17. 17.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  18. 18.
    Sutton, C., Mccallum, A.: Introduction to conditional random fields for relational learning. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning. MIT Press, Cambridge (2006)Google Scholar
  19. 19.
    yu Wu, T., chun Lian, C., jen Hsu, J.Y.: Joint recognition of multiple concurrent activities using factorial conditional random fields. In: 2007 AAAI Workshop on Plan, Activity, and Intent Recognition (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Carlo Nicolini
    • 1
  • Bruno Lepri
    • 2
  • Stefano Teso
    • 1
  • Andrea Passerini
    • 1
  1. 1.Dipartimento di Ingegneria e Scienza dell’InformazioneUniversità degli Studi di TrentoItaly
  2. 2.FBK-irstTrentoItaly

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