Skip to main content
SpringerLink
Log in
Book cover

International Conference on Ubiquitous Computing and Ambient Intelligence

UCAmI 2017: Ubiquitous Computing and Ambient Intelligence pp 381–393Cite as

Improving Activity Classification Using Ontologies to Expand Features in Smart Environments

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10586))

Abstract

Activity recognition is a promising field of research aiming to develop solutions within smart environments to provide relevant solutions on ambient assisted living, among others. The process of activity recognition aims to recognize the actions and goals of one or more person in a environment with a set of sensors are deployed, basing on the sensor data stream that capture a series of observations of actions and environmental conditions. This contributions presents the initial results from a new methodology that considers the use of ontologies to expand the set of feature vector, which is computed by using the sensor data stream, that is used in the process of activity recognition by data-driven approaches. The obtained results indicates that the use of extended feature vectors provided by the use of ontology offers a better accuracy regarding the original feature vectors used in the process of activity recognition with different data-driven approaches.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    http://ailab.wsu.edu/casas/datasets/ (last checked on April 19, 2017).

  2. 2.

    For simplicity, only the activation of the sensors has been taken into account for the experiment in Sect. 4.2. However, it is also possible to consider the deactivations of the sensors by just enabling a flag in the application developed for loading the dataset in the ontology.

  3. 3.

    The Weka implementation of the C4.5 classifier is called J48.

References

  1. Chandrasekaran, B., Josephson, J., Benjamins, V.: What are ontologies, and why do we need them? IEEE Intell. Syst. Appl. 14(1), 20–26 (1999)

    Article  Google Scholar 

  2. Chen, L., Hoey, J., Nugent, C., Cook, D., Yu, Z.: Sensor-based activity recognition. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(6), 790–808 (2012)

    Article  Google Scholar 

  3. Chen, L., Nugent, C.: Ontology-based activity recognition in intelligent pervasive environments. Int. J. Web Inf. Syst. 5(4), 410–430 (2009)

    Article  Google Scholar 

  4. Chen, L., Nugent, C., Okeyo, G.: An ontology-based hybrid approach to activity modeling for smart homes. IEEE Trans. Hum. Mach. Syst. 44(1), 92–105 (2014)

    Article  Google Scholar 

  5. Chen, L., Nugent, C., Wang, H.: A knowledge-driven approach to activity recognition in smart homes. IEEE Trans. Knowl. Data Eng. 24(6), 961–974 (2012)

    Article  Google Scholar 

  6. Cook, D., Schmitter-Edgecombe, M., Crandall, A., Sanders, C., Thomas, B.: Collecting and disseminating smart home sensor data in the CASAS project. In: Proceedings of the CHI Workshop on Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research (2009)

    Google Scholar 

  7. Horrocks, I., Patel-Schneider, P., Van Harmelen, F.: From SHIQ and RDF to OWL: the making of a web ontology language. Web Semant. 1(1), 7–26 (2003)

    Article  Google Scholar 

  8. Knijff, J., Frasincar, F., Hogenboom, F.: Domain taxonomy learning from text: the subsumption method versus hierarchical clustering. Data Knowl. Eng. 83, 54–69 (2013)

    Article  Google Scholar 

  9. Korhonen, I., Parkka, J., Van Gils, M.: Health monitoring in the home of the future. IEEE Eng. Med. Biol. Mag. 22(3), 66–73 (2003)

    Article  Google Scholar 

  10. Li, C., Lin, M., Yang, L., Ding, C.: Integrating the enriched feature with machine learning algorithms for human movement and fall detection. J. Supercomput. 67(3), 854–865 (2014)

    Article  Google Scholar 

  11. Nugent, C., Synnott, J., Santanna, A., Espinilla, M., Cleland, I., Banos, O., Lundström, J., Hallberg, J., Calzada, A.: An initiative for the creation of open datasets within the pervasive healthcare, pp. 180–183 (2016)

    Google Scholar 

  12. Rafferty, J., Chen, L., Nugent, C., Liu, J.: Goal lifecycles and ontological models for intention based assistive living within smart environments. Comput. Syst. Sci. Eng. 30(1), 7–18 (2015)

    Google Scholar 

  13. Sirin, E., Parsia, B., Grau, B., Kalyanpur, A., Katz, Y.: Pellet: a practical OWL-DL reasoner. Web Semant. 5(2), 51–53 (2007)

    Article  Google Scholar 

  14. Uschold, M., Gruninger, M.: Ontologies: principles, methods and applications. Knowl. Eng. Rev. 11(2), 93–136 (1996)

    Article  Google Scholar 

  15. Van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting, pp. 1–9 (2008)

    Google Scholar 

Download references

Acknowledgments

This project has received funding from the European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 734355 together the Spanish government by research project TIN2015-66524-P.

Author information

Authors and Affiliations

  1. Universidad de Cádiz, Cádiz, Spain

    Alberto Salguero

  2. Universidad de Jaén, Jaén, Spain

    Macarena Espinilla

Authors
  1. Alberto Salguero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Macarena Espinilla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alberto Salguero .

Editor information

Editors and Affiliations

  1. University of Santiago Chile, Santiago, Chile

    Sergio F. Ochoa

  2. Villanova University, Villanova, Pennsylvania, USA

    Pritpal Singh

  3. University of Castilla La Mancha, Ciudad Real, Spain

    José Bravo

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Salguero, A., Espinilla, M. (2017). Improving Activity Classification Using Ontologies to Expand Features in Smart Environments. In: Ochoa, S., Singh, P., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science(), vol 10586. Springer, Cham. https://doi.org/10.1007/978-3-319-67585-5_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67585-5_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67584-8

  • Online ISBN: 978-3-319-67585-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation