Advertisement

Semantic-Based Sensor Data Segmentation

  • Liming ChenEmail author
  • Chris D. Nugent
Chapter

Abstract

This chapter introduces an approach to semantically distinguishing individual sensor events directly to relevant constituent activities in the context of interleaved and concurrent activity recognition. It first reviews related work and highlights the needs and challenges of data segmentation of composite activity recognition. It then proposes a semiotic theory inspired ontological model, capturing generic knowledge and inhabitant-specific preferences for conducting ADLs to support the segmentation process. Following this, the chapter presents a multithread semantic based segmentation algorithm for dynamic sensor segmentation of composite activities. Finally, the chapter describes an example case study to evaluate and demonstrate the proposed approach in an implemented system prototype.

References

  1. 1.
    Faria DR, Vieira M, Premebida C, Nunes U (2015) Probabilistic human daily activity recognition towards robot-assisted living. 2015 24th IEEE international symposium on robot and human interactive communication (RO-MAN), pp 582–587Google Scholar
  2. 2.
    Zhong Y (2017) A theory of semantic information. China Commun 14:1–17CrossRefGoogle Scholar
  3. 3.
    Tarski A (1944) The semantic conception of truth: and the foundations of semantics. http://www.jstor.org/stable/2102968?origin=crossrefMathSciNetCrossRefGoogle Scholar
  4. 4.
    Vickers P (2013) Understanding visualisation: a formal foundation using category theory and semiotics. IEEE Trans Vis Comput Graph X, 1–14Google Scholar
  5. 5.
    Wang Y (2017) Formal rules for concept and semantics manipulations in cognitive linguistics and machine learning. In: 2017 IEEE 16th international conference on cognitive informatics cognitive computing (ICCI*CC), pp 43–50Google Scholar
  6. 6.
    Rafferty J, Nugent CD, Liu J, Chen L (2016) From activity recognition to intention recognition for assisted living within smart homes. IEEE Trans Human-Mach Syst 1–12Google Scholar
  7. 7.
    Meditskos G, Dasiopoulou S, Kompatsiaris I (2015) MetaQ: a knowledge-driven framework for context-aware activity recognition combining SPARQL and OWL 2 activity patterns. Pervasive Mob ComputGoogle Scholar
  8. 8.
    Della Valle E, Grossniklaus M (2010) C-SPARQL: a continuous query language for RDF data streams. Int J Semant Comput 04:3–25Google Scholar
  9. 9.
    Okeyo G, Chen L, Wang H, Sterritt R (2012) A hybrid ontological and temporal approach for composite activity modelling. In: Proceedings - 12th IEEE international conference on trust, security and privacy in computing, trustcom-2012 - 11th IEEE int. conference on ubiquitous computing and communications, IUCC-2012, pp 1763–1770Google Scholar
  10. 10.
    Okeyo G, Chen L, Wang H (2014) Combining ontological and temporal formalisms for composite activity modelling and recognition in smart homes. Futur Gener Comput Syst 39:29–43CrossRefGoogle Scholar
  11. 11.
    Skillen KL, Chen L, Nugent CD, Donnelly MP, Burns W, Solheim I (2014) Ontological user modelling and semantic rule-based reasoning for personalisation of help-on-demand services in pervasive environments. Futur Gener Comput Syst 34:97–109CrossRefGoogle Scholar
  12. 12.
    Culmone R, Giuliodori P, Quadrini M (2015) Human activity recognition using a semantic ontology-based framework. Int J Adv Intell Syst 8:159–168Google Scholar
  13. 13.
    Naeem U (2015) Activities of daily life recognition using process representation modelling to support intention analysis. Int J Pervasive Comput Commun 11:347CrossRefGoogle Scholar
  14. 14.
    Hong X, Nugent CD (2013) Segmenting sensor data for activity monitoring in smart environments. Pers Ubiquitous Comput 17:545–559CrossRefGoogle Scholar
  15. 15.
    Abburu S (2012) A survey on ontology reasoners and comparison. Int J Comput Appl 57:33–39Google Scholar
  16. 16.
    Dentler K, Cornet R, Ten Teije A, De Keizer N (2011) Comparison of reasoners for large ontologies in the OWL 2 EL profile. Semant Web 2:71–87Google Scholar
  17. 17.
    De Giacomo G, Lenzerini M (1996) TBox and ABox reasoning in expressive description logics. In: Proceedings of fifth international conference on the principles of knowledge representation and reasoning, pp 316–327 (1996)Google Scholar
  18. 18.
    Triboan D, Chen L, Chen F, Wang Z (2017) Semantic segmentation of real-time sensor data stream for complex activity recognition. Pers, Ubiquitous ComputCrossRefGoogle Scholar
  19. 19.
    Triboan D, Chen L, Chen F, Fallmann S, Psychoula I (2017) Real-time sensor observation segmentation for complex activity recognition within smart environments. In: 2017 IEEE 14th international conference on ubiquitous intelligence and computing (UIC 2017), San FranciscoGoogle Scholar
  20. 20.
    Riboni D, Bettini C (2011) OWL 2 modeling and reasoning with complex human activities. Pervasive Mob Comput 7(3):379–395CrossRefGoogle Scholar
  21. 21.
    Stanford University, University, S ProtégéGoogle Scholar
  22. 22.
    Volz R, Staab S, Motik B (2003) Incremental maintenance of materialized ontologies. Lect Notes Comput Sci 2888(2003):707–724CrossRefGoogle Scholar
  23. 23.
    W3C: SPIN - overview and motivation. https://www.w3.org/Submission/spin-overview/
  24. 24.
    Cuenca Grau B, Halaschek-Wiener C, Kazakov Y (2007) History matters: incremental ontology reasoning using modules. Lecture notes in computer science (including subseries Lecture notes in artificial intelligence (LNAI) and lecture notes in bioinformatics). LNCS, vol 4825, pp 183–196CrossRefGoogle Scholar
  25. 25.
    Ren Y, Pan JZ, Guclu I, Kollingbaum M (2016) A combined approach to incremental reasoning for EL ontologies. Lecture notes in computer science (including subseries Lecture notes in artificial intelligence (LNAI) and lecture notes in bioinformatics). LNCS, vol 9898, pp 167–183CrossRefGoogle Scholar
  26. 26.
    Peters M, Brink C, Sachweh S, Zündorf A (2014) Scaling parallel rule-based reasoning. Lecture notes in computer science (including subseries Lecture notes in artificial intelligence (LNAI) and lecture notes in bioinformatics). LNCS, vol 8465, pp 270–285Google Scholar
  27. 27.
    Ponge J Fork and join: java can excel at painless parallel programming too!. http://www.oracle.com/technetwork/articles/java/fork-join-422606.html

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

Personalised recommendations