A Hybrid Approach to Activity Modelling

  • Liming ChenEmail author
  • Chris D. Nugent


This chapter introduces an ontology-based hybrid approach to activity modeling that combines domain knowledge-based model specification and data-driven model learning. Central to the approach is an iterative process that begins with “seed” activity models created by ontological engineering. The “seed” models are then put in use, and subsequently evolved through incremental activity discovery and model update. While our previous work has detailed ontological activity modeling and activity recognition, this chapter focuses on the systematic hybrid approach and associated methods and inference rules for learning new activities and user activity profiles. An example case study has been used to demonstrate and evaluate the activity learning algorithms and mechanisms through which well-designed experiments in a feature-rich assistive living system.


  1. 1.
    WHO | International Classification of Functioning, Disability and Health (ICF). WHO (2018)Google Scholar
  2. 2.
    Liao L, Fox D, Kautz H (2007) Hierarchical conditional random fields for GPS-based activity recognition. In: Thrun S, Brooks R, Durrant-Whyte H (eds) Robotics research. Springer, Berlin, pp 487–506CrossRefGoogle Scholar
  3. 3.
    Lester J, Choudhury T, Kern N, Borriello G, Hannaford B (2005) A hybrid discriminative/generative approach for modeling human activities. In: Proceedings of the 19th international joint conference on artificial intelligence. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 766–772Google Scholar
  4. 4.
    Hu DH, Yang Q (2011) Transfer learning for activity recognition via sensor mapping. In: IJCAI international joint conference on artificial intelligenceGoogle Scholar
  5. 5.
    Rashidi P, Cook DJ (2011) Activity knowledge transfer in smart environments. Pervasive Mob ComputGoogle Scholar
  6. 6.
    Van Kasteren TLM, Englebienne G, Kröse BJA (2010) Transferring knowledge of activity recognition across sensor networks. In: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)Google Scholar
  7. 7.
    Cook D, Feuz KD, Krishnan NC (2013) Transfer learning for activity recognition: a survey. Knowl Inf Syst 36:537–556CrossRefGoogle Scholar
  8. 8.
    Perkowitz M, Philipose M, Fishkin K, Patterson DJ (2004) Mining models of human activities from the web. In: Proceedings of the 13th conference on World Wide Web - WWW ’04Google Scholar
  9. 9.
    Tapia EM, Choudhury T, Philipose M (2006) Building reliable activity models using hierarchical shrinkage and mined ontology. In: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)Google Scholar
  10. 10.
    Mann CJH (2003) The description logic handbook – theory, implementation and applications. Kybernetes (2003)Google Scholar
  11. 11.
    Jain AK, Dubes RC (1988) Algorithms for clustering data, Englewood Cliffs, N.J.: Prentice Hall, ISBN:0-13-022278-XGoogle Scholar
  12. 12.
    Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd ed., Elsevier, ISBN:978-0-12-374856-0Google Scholar
  13. 13.
    Hong X, Nugent CD (2013) Segmenting sensor data for activity monitoring in smart environments. Pers Ubiquitous Comput 17:545–559CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and InformaticsDe Montfort UniversityLeicesterUK
  2. 2.School of ComputingUlster UniversityBelfastUK

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