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A Continuous-Time Model-Based Approach to Activity Recognition for Ambient Assisted Living

  • Laura Carnevali
  • Christopher Nugent
  • Fulvio Patara
  • Enrico Vicario
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9259)

Abstract

In Ambient Assisted Living (AAL), Activity Recognition (AR) plays a crucial role in filling the semantic gap between sensor data and interpretation needed at the application level. We propose a quantitative model-based approach to on-line prediction of activities that takes into account not only the sequencing of events but also the continuous duration of their inter-occurrence times: given a stream of time-stamped and typed events, online transient analysis of a continuous-time stochastic model is used to derive a measure of likelihood for the currently performed activity and to predict its evolution until the next event; while the structure of the model is predefined, its actual topology and stochastic parameters are automatically derived from the statistics of observed events. The approach is validated with reference to a public data set widely used in applications of AAL, providing results that show comparable performance with state-of-the-art offline approaches, namely Hidden Markov Models (HMM) and Conditional Random Fields (CRF).

Keywords

Ambient Assisted Living (AAL) Activity Recognition (AR) Continuous-time stochastic models Transient analysis On-line prediction Process enhancement 

References

  1. 1.
    Amparore, E.G., Buchholz, P., Donatelli, S.: A structured solution approach for markov regenerative processes. In: Norman, G., Sanders, W. (eds.) QEST 2014. LNCS, vol. 8657, pp. 9–24. Springer, Heidelberg (2014) Google Scholar
  2. 2.
    Avci, U., Passerini, A.: Improving activity recognition by segmental pattern mining. IEEE Trans. Knowl. Data Eng. 26(4), 889–902 (2014)CrossRefGoogle Scholar
  3. 3.
    Bartocci, E., Bortolussi, L., Sanguinetti, G.: Data-driven statistical learning of temporal logic properties. In: Legay, A., Bozga, M. (eds.) FORMATS 2014. LNCS, vol. 8711, pp. 23–37. Springer, Heidelberg (2014) Google Scholar
  4. 4.
    Berthomieu, B., Diaz, M.: Modeling and verification of time dependent systems using time petri nets. IEEE Trans. Soft. Eng. 17(3), 259–273 (1991)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Bobbio, A., Telek, M.: Markov regenerative SPN with non-overlapping activity cycles. In: International Computer Performance and Dependability Symposium, pp. 124–133 (1995)Google Scholar
  6. 6.
    Bobbio, A., Horváth, A., Telek, M.: Matching three moments with minimal acyclic phase type distributions. Stoch. Models 21(2–3), 303–326 (2005)CrossRefMathSciNetzbMATHGoogle Scholar
  7. 7.
    Bucci, G., Carnevali, L., Ridi, L., Vicario, E.: Oris: a tool for modeling, verification and evaluation of real-time systems. Int. J. Softw. Tools Technol. Transfer 12(5), 391–403 (2010)CrossRefGoogle Scholar
  8. 8.
    Buchholz, R., Krull, C., Strigl, T., Horton, G.: Using hidden non-markovian models to reconstruct system behavior in partially-observable systems. In: International ICST Conference on Simulation Tools and Techniques, p. 86 (2010)Google Scholar
  9. 9.
    Choi, H., Kulkarni, V.G., Trivedi, K.S.: Markov regenerative stochastic Petri nets. Perf. Eval. 20(1–3), 337–357 (1994)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Ciardo, G., German, R., Lindemann, C.: A characterization of the stochastic process underlying a stochastic Petri net. IEEE Trans. Softw. Eng. 20(7), 506–515 (1994)CrossRefGoogle Scholar
  11. 11.
    Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: technologies, applications, and opportunities. Pervasive Mob. Comput. 5(4), 277–298 (2009)CrossRefGoogle Scholar
  12. 12.
    Horváth, A., Paolieri, M., Ridi, L., Vicario, E.: Transient analysis of non-Markovian models using stochastic state classes. Perform. Eval. 69(7–8), 315–335 (2012)CrossRefGoogle Scholar
  13. 13.
    Horváth, A., Telek, M.: PhFit: a general phase-type fitting tool. In: Field, T., Harrison, P.G., Bradley, J., Harder, U. (eds.) TOOLS 2002. LNCS, vol. 2324, pp. 82–91. Springer, Heidelberg (2002) Google Scholar
  14. 14.
    Katz, S., Downs, T.D., Cash, H.R., Grotz, R.C.: Progress in development of the index of ADL. The Gerontologist 10(1 Part 1), 20–30 (1970)CrossRefGoogle Scholar
  15. 15.
    Mans, R.S., Schonenberg, M.H., Song, M., van der Aalst, W.M.P., Bakker, P.J.M.: Application of process mining in healthcare - a case study in a dutch hospital. In: Fred, A., Filipe, J., Gamboa, H. (eds.) BIOSTEC 2011. CCIS, vol. 273, pp. 425–438. Springer, Heidelberg (2009)Google Scholar
  16. 16.
    Mitchell, C.D., Jamieson, L.H.: Modeling duration in a hidden Markov model with the exponential family. IEEE Int. Conf. Acoust. Speech Signal Process. 2, 331–334 (1993)Google Scholar
  17. 17.
    Neuts, M.F.: Matrix Geometric Solutions in Stochastic Models. Johns Hopkins University Press, London (1981)zbMATHGoogle Scholar
  18. 18.
    Patterson, D.J., Liao, L., Fox, D., Kautz, H.: Inferring high-level behavior from low-level sensors. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864, pp. 73–89. Springer, Heidelberg (2003) CrossRefGoogle Scholar
  19. 19.
    Rashidi, P., Cook, D.J.: Keeping the resident in the loop: adapting the smart home to the user. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 39(5), 949–959 (2009)CrossRefGoogle Scholar
  20. 20.
    Rashidi, P., Cook, D.J., Holder, L.B., Schmitter-Edgecombe, M.: Discovering activities to recognize and track in a smart environment. IEEE Trans. Knowl. Data Eng. 23(4), 527–539 (2011)CrossRefGoogle Scholar
  21. 21.
    Reinecke, P., Krauß, T., Wolter, K.: Phase-type fitting using hyperstar. In: Balsamo, M.S., Knottenbelt, W.J., Marin, A. (eds.) EPEW 2013. LNCS, vol. 8168, pp. 164–175. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  22. 22.
    Rogge-Solti, A., Weske, M.: Prediction of remaining service execution time using stochastic petri nets with arbitrary firing delays. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 389–403. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  23. 23.
    Trivedi, K.S.: Probability and Statistics with Reliability, Queuing, and Computer Science Applications. John Wiley and Sons, New York (2001) Google Scholar
  24. 24.
    van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  25. 25.
    van der Aalst, W.M.P., Reijers, H.A., Weijters, A.J.M.M., van Dongen, B.F., Alves De Medeiros, A.K., Song, M., Verbeek, H.M.W.: Business process mining: an industrial application. Inf. Syst. 32(5), 713–732 (2007)CrossRefGoogle Scholar
  26. 26.
    van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W.E., Weijters, A.J.M.M.T., van der Aalst, W.M.P.: The ProM framework: a new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  27. 27.
    van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In: Proceedings of the International Conference on Ubiquitous Computing, UbiComp 2008, pp. 1–9. ACM, New York, NY, USA (2008)Google Scholar
  28. 28.
    Vicario, E., Sassoli, L., Carnevali, L.: Using stochastic state classes in quantitative evaluation of dense-time reactive systems. IEEE Trans. Softw. Eng. 35(5), 703–719 (2009)CrossRefGoogle Scholar
  29. 29.
    Whitt, W.: Approximating a point process by a renewal process, I: two basic methods. Oper. Res. 30(1), 125–147 (1982)CrossRefMathSciNetzbMATHGoogle Scholar
  30. 30.
    Ye, J., Dobson, S., McKeever, S.: Situation identification techniques in pervasive computing: a review. Pervasive Mob. Comput. 8(1), 36–66 (2012)CrossRefGoogle Scholar
  31. 31.
    Zimmermann, A.: Dependability evaluation of complex systems with TimeNET. In: Proceedings of the International Workshop on Dynamic Aspects in Dependability Models for Fault-Tolerant Systems, pp. 33–34 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Laura Carnevali
    • 1
  • Christopher Nugent
    • 2
  • Fulvio Patara
    • 1
  • Enrico Vicario
    • 1
  1. 1.Department of Information EngineeringUniversity of FlorenceFlorenceItaly
  2. 2.School of Computing and MathematicsUniversity of UlsterBelfastUK

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