Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2015: Intelligent Data Engineering and Automated Learning – IDEAL 2015 pp 378–386Cite as

Neural Network-Based User-Independent Physical Activity Recognition for Mobile Devices

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9375))

Abstract

Activity recognition using sensors of mobile devices is a topic of interest of many research efforts. It has been established that user-specific training gives good accuracy in accelerometer-based activity recognition. In this paper we test a different approach: offline user-independent activity recognition based on pretrained neural networks with Dropout. Apart from satisfactory recognition accuracy that we prove in our tests, we foresee possible advantages in removing the need for users to provide labeled data and also in the security of the system. These advantages can be the reason for applying this approach in practice, not only in mobile phones but also in other embedded devices.

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   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

References

  1. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: Bravo, J., Hervás, R., Rodríguez, M. (eds.) IWAAL 2012. LNCS, vol. 7657, pp. 216–223. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  2. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN (2013)

    Google Scholar 

  3. Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., Havinga, P.: Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: a survey. In: 2010 23rd international conference on Architecture of computing systems (ARCS), pp. 1–10. VDE (2010)

    Google Scholar 

  4. Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)

    Article  MATH  Google Scholar 

  5. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  6. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  7. Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)

  8. Huang, L., Joseph, A.D., Nelson, B., Rubinstein, B.I., Tygar, J.: Adversarial machine learning. In: Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, pp. 43–58. ACM (2011)

    Google Scholar 

  9. Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SigKDD Explor. Newslett. 12(2), 74–82 (2011)

    Article  Google Scholar 

  10. Plötz, T., Hammerla, N.Y., Olivier, P.: Feature learning for activity recognition in ubiquitous computing. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, vol. 22, p. 1729 (2011)

    Google Scholar 

  11. Prudêncio, J., Aguiar, A., Lucani, D.: Physical activity recognition from smartphone embedded sensors. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds.) IbPRIA 2013. LNCS, vol. 7887, pp. 863–872. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  12. Siirtola, P., Röning, J.: Recognizing human activities user-independently on smartphones based on accelerometer data. Int. J. Interact. Multimed. Artif. Intell. 1(5), 38–45 (2012)

    Google Scholar 

  13. Siirtola, P., Roning, J.: Ready-to-use activity recognition for smartphones. In: 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 59–64. IEEE (2013)

    Google Scholar 

  14. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  15. Weiss, G.M., Lockhart, J.W.: The impact of personalization on smartphone-based activity recognition. In: AAAI Workshop on Activity Context Representation: Techniques and Languages (2012)

    Google Scholar 

  16. Zeng, M., Nguyen, L.T., Yu, B., Mengshoel, O.J., Zhu, J., Wu, P., Zhang, J.: Convolutional neural networks for human activity recognition using mobile sensors

    Google Scholar 

Download references

Acknowledgment

The research leading to these results was supported by the Bavarian State Ministry of Education, Science and the Arts as part of the FORSEC research association.

Author information

Authors and Affiliations

  1. Faculty of Informatics, Technische Universität München, Boltzmannstraße 3, 85748, Garching, Germany

    Bojan Kolosnjaji & Claudia Eckert

Authors
  1. Bojan Kolosnjaji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Claudia Eckert
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bojan Kolosnjaji .

Editor information

Editors and Affiliations

  1. Wroclaw University of Technology, Wroclaw, Poland

    Konrad Jackowski

  2. Department of Systems, Wroclaw University of Technology, Wroclaw, Poland

    Robert Burduk

  3. Wroclaw Univ of Tech, Wroclaw, Poland

    Krzysztof Walkowiak

  4. Wroclaw University of Technology, Faculty of Electronics, Wroclaw, Poland

    Michal Wozniak

  5. School of Electrical & Electronic E, University of Manchester, Manchester, United Kingdom

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Kolosnjaji, B., Eckert, C. (2015). Neural Network-Based User-Independent Physical Activity Recognition for Mobile Devices. In: Jackowski, K., Burduk, R., Walkowiak, K., Wozniak, M., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2015. IDEAL 2015. Lecture Notes in Computer Science(), vol 9375. Springer, Cham. https://doi.org/10.1007/978-3-319-24834-9_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-24834-9_44

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24833-2

  • Online ISBN: 978-3-319-24834-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation