Movatterモバイル変換


[0]ホーム

URL:


Skip to main content

Advertisement

Springer Nature Link
Log in

Generation of a Partitioned Dataset with Single, Interleave and Multioccupancy Daily Living Activities

  • Conference paper
  • First Online:

Abstract

The advances in electronic devices have entailed the development of smart environments which have the aim to help and make easy the life of their inhabitants. In this kind of environments, an important task is the process of activity recognition of an inhabitant in the environment in order to anticipate the occupant necessities and to adapt such smart environment. Due to the cost to checking activity recognition approaches in real environments, usually, they use datasets generated from smart environments. Although there are many datasets for activity recognition in smart environments, it is difficult to find single, interleaved or multioccupancy activity datasets, or combinations of these classes of activities according to the researchers’ needs. In this work, the design and development of a complete dataset with 14 sensors and 9 different activities daily living is described, being this dataset divided into partitions with different classes of activities.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • 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

Similar content being viewed by others

Notes

  1. 1.

    http://ailab.wsu.edu/casas/datasets/ (last checked on August 27, 2015).

  2. 2.

    www.tynetec.co.uk (last checked on August 27, 2015).

  3. 3.

    http://ceatic.ujaen.es/smartlab (last checked on August 27, 2015).

References

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

    Article  Google Scholar 

  2. 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 

  3. Chen, L., Nugent, C.D., 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 

  4. 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, pp. 1–7 (2009)

    Google Scholar 

  5. Cook, D.J., Schmitter-Edgecombe, M., et al.: Assessing the quality of activities in a smart environment. Methods Inf. Med.48(5), 480–485 (2009)

    Article  Google Scholar 

  6. Gu, T., Wang, L., Wu, Z., Tao, X., Lu, J.: A pattern mining approach to sensor-based human activity recognition. IEEE Trans. Knowl. Data Eng.23(9), 1359–1372 (2011)

    Article  Google Scholar 

  7. Jurek, A., Nugent, C., Bi, Y., Wu, S.: Clustering-based ensemble learning for activity recognition in smart homes. Sensors14(7), 12285–12304 (2014)

    Article  Google Scholar 

  8. Lepri, B., Mana, N., Cappelletti, A., Pianesi, F., Zancanaro, M.: What is happening now? detection of activities of daily living from simple visual features. Pers. Ubiquit. Comput.14(8), 749–766 (2010)

    Article  Google Scholar 

  9. Li, C., Lin, M., Yang, L.T., 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 

  10. Moshtaghi, M., Zukerman, I., Russell, R.: Statistical models for unobtrusively detecting abnormal periods of inactivity in older adults. User Model. User-Adap. Inter.25(3), 231–265 (2015)

    Article  Google Scholar 

  11. Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., Forster, K., Troster, G., Lukowicz, P., Bannach, D., Pirkl, G., Ferscha, A., et al.: Collecting complex activity datasets in highly rich networked sensor environments. In: 2010 Seventh International Conference on Networked Sensing Systems (INSS), pp. 233–240. IEEE (2010)

    Google Scholar 

  12. Singla, G., Cook, D.J., Schmitter-Edgecombe, M.: Tracking activities in complex settings using smart environment technologies. Int. J. Biosci. Psychiatry Technol. (IJBSPT)1(1), 25 (2009)

    Google Scholar 

  13. Singla, G., Cook, D.J., Schmitter-Edgecombe, M.: Recognizing independent and joint activities among multiple residents in smart environments. J. Ambient Intell. Humanized Comput.1(1), 57–63 (2010)

    Article  Google Scholar 

  14. Streitz, N., Nixon, P.: The disappearing computer. Commun. ACM48(3), 32–35 (2005)

    Article  Google Scholar 

  15. Tapia, E.M., Intille, S.S., Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  16. Van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 1–9. ACM (2008)

    Google Scholar 

Download references

Acknowledgments

This contribution was supported by Research Projects TIN-2012-31263, CEATIC-2013-001, UJA2014/06/14 and by the Doctoral School of the University of Jaén. Invest Northern Ireland is acknowledge for partially supporting this project under the Competence Centre Program Grant RD0513853 - Connected Health Innovation Centre.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Jaén, Jaén, Spain

    Francisco J. Quesada, Francisco Moya, Javier Medina, Luis Martínez & Macarena Espinilla

  2. School of Computing and Mathematics, University of Ulster, Jordanstown, BT37 0QB, UK

    Chris Nugent

Authors
  1. Francisco J. Quesada
  2. Francisco Moya
  3. Javier Medina
  4. Luis Martínez
  5. Chris Nugent
  6. Macarena Espinilla

Corresponding author

Correspondence toMacarena Espinilla.

Editor information

Editors and Affiliations

  1. Universidad de Alicante, Alicante, Spain

    Juan M. García-Chamizo

  2. University of Calabria, Rende, Italy

    Giancarlo Fortino

  3. Universidad de Chile, Santiago, Chile

    Sergio F. Ochoa

Rights and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Quesada, F.J., Moya, F., Medina, J., Martínez, L., Nugent, C., Espinilla, M. (2015). Generation of a Partitioned Dataset with Single, Interleave and Multioccupancy Daily Living Activities. In: García-Chamizo, J., Fortino, G., Ochoa, S. (eds) Ubiquitous Computing and Ambient Intelligence. Sensing, Processing, and Using Environmental Information. UCAmI 2015. Lecture Notes in Computer Science(), vol 9454. Springer, Cham. https://doi.org/10.1007/978-3-319-26401-1_6

Download citation

Publish with us

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • 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


[8]ページ先頭

©2009-2025 Movatter.jp