Contextualized Behavior Patterns for Ambient Assisted Living

  • Paula Lago
  • Claudia Jiménez-Guarín
  • Claudia Roncancio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9277)


Human behavior learning plays an important role in ambient assisted living since it enables service personalization. Current work in human behavior learning do not consider the context under which a behavior occurs, which hides some behaviors that are frequent only under certain conditions. In this work, we present the notion of a contextualized behavior pattern, which describes a behavior pattern with the context in which it occurs (i.e. nap when raining) and propose an algorithm for finding these patterns in a data stream. This is our main contribution. These patterns help to better understand the routine of a user in a smart environment, as is evidenced when testing with a public dataset. This algorithm could be used to learn behaviors from users in an ambient assisted living environment in order to send alarms when behavior changes occur.


Human behavior learning Sequential patterns Context analysis Personalization Data streams 


  1. 1.
    Monekosso, D.N., Remagnino, P.: Behavior analysis for assisted living. IEEE Trans. Autom. Sci. Eng. 7, 879–886 (2010)CrossRefGoogle Scholar
  2. 2.
    Rodríguez, N.D., Cuéllar, M.P., Lilius, J., Calvo-Flores, M.D.: A fuzzy ontology for semantic modelling and recognition of human behaviour. Knowl. Based Syst. 66, 46–60 (2014)CrossRefGoogle Scholar
  3. 3.
    Iglesias, J.A., Angelov, P., Ledezma, A., Sanchis, A.: Creating evolving user behavior profiles automatically. IEEE Trans. Knowl. Data Eng. 24, 854–867 (2012)CrossRefGoogle Scholar
  4. 4.
    Chua, S., Marsland, S.: Unsupervised learning of human behaviours. In: Twenty-Fifth AAAI Conference, pp. 319–324 (2011)Google Scholar
  5. 5.
    Pei, J.P.J., Han, J.H.J., Mortazavi-Asl, B., Pinto, H., Chen, Q.C.Q., Dayal, U., Hsu, M.-C.H.M.-C.: PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of the 17th International Conference Data Engineering (2001)Google Scholar
  6. 6.
    Cook, D.J., Crandall, A.S., Thomas, B.L., Krishnan, N.C.: CASAS : a smart home in a box. IEEE Comput. 46, 62–69 (2013)CrossRefGoogle Scholar
  7. 7.
    Turaga, P., Member, S., Chellappa, R., Subrahmanian, V.S., Udrea, O.: Mach. Recogn. Hum. Activities Surv. 18, 1473–1488 (2008)Google Scholar
  8. 8.
    Ryoo, M.S., Aggarwal, J.K.: Recognition of composite human activities through context-free grammar based representation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1709–1716 (2006)Google Scholar
  9. 9.
    Ordonez, F.J., Englebienne, G., de Toledo, P., van Kasteren, T., Sanchis, A., Kröse, B.: In-home activity recognition: bayesian inference for hidden markov models. IEEE Pervasive Comput. 13, 67–75 (2014)CrossRefGoogle Scholar
  10. 10.
    Forkan, A.R.M., Khalil, I., Tari, Z., Foufou, S., Bouras, A.: A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living. Pattern Recognit. 48, 628–641 (2014)CrossRefGoogle Scholar
  11. 11.
    Rieping, K., Englebienne, G., Kröse, B.: Behavior analysis of elderly using topic models. Pervasive Mobile Comput. 15, 181–199 (2014)CrossRefGoogle Scholar
  12. 12.
    Seiter, J., Amft, O., Rossi, M., Tröster, G.: Discovery of activity composites using topic models: An analysis of unsupervised methods. Pervasive Mob. Comput. 15, 215–227 (2014)CrossRefGoogle Scholar
  13. 13.
    Aztiria, A., Augusto, J.C., Basagoiti, R., Izaguirre, A.: Accurate temporal relationships in sequences of user behaviours in intelligent environments. In: Augusto, J.C., Corchado, J.M., Novais, P., Analide, C. (eds.) Ambient Intelligence and Future Trends-International Symposium on Ambient Intelligence (ISAm I 2010), pp. 19–27. Springer, Berlin Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Srinivasan, V., Moghaddam, S., Mukherji, A., Rachuri, K.K., Xu, C., Tapia, E.M.: MobileMiner. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2014 Adjunct), pp. 389–400. ACM Press, New York (2014)Google Scholar
  15. 15.
    Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: Technologies, applications, and opportunities. Pervasive Mob. Comput. 5, 277–298 (2009)CrossRefGoogle Scholar
  16. 16.
    Mabroukeh, N.R., Ezeife, C.I.: A taxonomy of sequential pattern mining algorithms. ACM Comput. Surv. 43, 1–41 (2010)CrossRefGoogle Scholar
  17. 17.
    Mooney, C.H., Roddick, J.F.: Sequential pattern mining – approaches and algorithms. ACM Comput. Surv. 45, 19 (2013)CrossRefMATHGoogle Scholar
  18. 18.
    Mallick, B., Garg, D., Grover, P.S.: Incremental mining of sequential patterns : Progress and challenges. Intell. Data Anal. 17, 507–530 (2013)Google Scholar
  19. 19.
    Soliman, A.F., Ebrahim, G.a., Mohammed, H.K.: SPEDS: a framework for mining sequential patterns in evolving data streams. In: Pacific Rim Conference on Communications, Computers Signal Process, pp. 464–469 (2011)Google Scholar
  20. 20.
    Moshtaghi, M., Zukerman, I., Russell, R.A.: Statistical models for unobtrusively detecting abnormal periods of inactivity in older adults. User Model User adapt. Interact. 25, 231–265 (2015)CrossRefGoogle Scholar
  21. 21.
    Pei, J., Han, J., Wang, W.: Constraint-based sequential pattern mining: the pattern-growth methods. J. Intell. Inf. Syst. 28, 133–160 (2007)CrossRefGoogle Scholar
  22. 22.
    Saleh, B., Masseglia, F.: Discovering frequent behaviors: time is an essential element of the context. Knowl. Inf. Syst. 28, 311–331 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Paula Lago
    • 1
  • Claudia Jiménez-Guarín
    • 1
  • Claudia Roncancio
    • 2
  1. 1.Systems and Computing Engineering Department, School of EngineeringUniversidad de Los AndesBogotáColombia
  2. 2.LIGUniversity GrenobleGrenobleFrance

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