Skip to main content
SpringerLink
Log in

Application of Neural Network for Human Actions Recognition

  • Conference paper
  • First Online:
Computational Intelligence and Intelligent Systems (ISICA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 575))

  • 1648 Accesses

Abstract

In this paper we have proposed human actions recognition methodology. The main novelty of this paper is application of neural network (NN) trained with the parallel stochastic gradient descent to perform classification task on multi-dimensional time-varying signal. The original motion-capture data consisted of 20 time-varying three-dimensional body joint coordinates acquired with Kinect controller is preprocessed to 9-dimensional angle-based time-varying features set. The data is resampled to the uniform length with cubic spline interpolation after which each action is represented by 60 samples and eventually 540 (60 × 9) variables are presented to input layer of NN. The dataset we used in our experiment consists of recordings for 14 participants that perform nine types of popular gym exercises (totally 770 actions samples). The averaged recognition rate in k-fold cross validation for different actions classes were between 95.6 % ± 9.5 % to even 100 %.

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

Access this chapter

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

Institutional subscriptions

References

  1. Li, S., Liu, Z.-Q., Chan, A.B.: Heterogeneous multi-task learning for human pose estimation with deep convolutional neural network. Int. J. Comput. Vis. 113, 19–36 (2015)

    Article  MathSciNet  Google Scholar 

  2. Jiu, M., Wolf, C., Garcia, C., Baskurt, A.: Supervised learning and codebook optimization for bag-of-words models. Cogn. Comput. 4, 409–419 (2012)

    Article  Google Scholar 

  3. Guo, W., Chen, G.: Human action recognition via multi-task learning base on spatial–temporal feature. Inf. Sci. 320(1), 418–428 (2015)

    Article  Google Scholar 

  4. Díaz-Más, L., Muñoz-Salinas, R., Madrid-Cuevas, F.J., Medina-Carnicer, R.: Three-dimensional action recognition using volume integrals. Pattern Anal. Appl. 15, 289–298 (2012)

    Article  MathSciNet  Google Scholar 

  5. Liu, L., Shao, L., Rockett, P.: Boosted key-frame selection and correlated pyramidal motion-feature representation for human action recognition. Pattern Recogn. 46, 1810–1818 (2013)

    Article  Google Scholar 

  6. Zhu, F., Shao, L., Lin, M.: Multi-view action recognition using local similarity random forests and sensor fusion. Pattern Recogn. Lett. 34, 20–24 (2013)

    Article  Google Scholar 

  7. del Rincón, J.M., Santofimia, M.J., Nebel, J.-C.: Common-sense reasoning for human action recognition. Pattern Recogn. Lett. 34, 1849–1860 (2013)

    Article  Google Scholar 

  8. Yang, X., Tian, Y.: Effective 3D action recognition using EigenJoints. J. Vis. Commun. Image Represent. 25, 2–11 (2014)

    Article  Google Scholar 

  9. Chen, G., Clarke, D., Giuliani, M., Gaschler, A., Knoll, A.: Combining unsupervised learning and discrimination for 3D action recognition. Sig. Process. 110, 67–81 (2015)

    Article  Google Scholar 

  10. Hachaj, T., Ogiela, M.R.: Full body movements recognition – unsupervised learning approach with heuristic R-GDL method. Digit. Sig. Process. 46, 239–252 (2015)

    Article  MathSciNet  Google Scholar 

  11. Hachaj, T., Ogiela, M.R.: Rule-based approach to recognizing human body poses and gestures in real time. Multimedia Syst. 20, 81–99 (2014)

    Article  Google Scholar 

  12. Recht, B., Re, C., Wright, S., Niu, F.: Hogwild: a lock-free approach to parallelizing stochastic gradient descent. In: Shawe-Taylor, J., Zemel, R.S., Bartlett, P., Pereira, F.C.N., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 24, pp. 693–701 (2011)

    Google Scholar 

  13. Hachaj, T., Ogiela, M.R., Koptyra, K.: Effectiveness comparison of Kinect and Kinect 2 for recognition of Oyama karate techniques. NBiS 2015 - The 18-th International Conference on Network-Based Information Systems (NBiS 2015), September 2–4, Taipei, Taiwan, pp. 332–337 (2015). doi:10.1109/NBiS.2015.51

  14. Candel, A., Parmer, V.: Deep Learning with H2O, Published by H2O, (2015). http://leanpub.com/deeplearning. Accessed 8 August 2015

  15. Bengio, Y.: Learning deep architectures for AI. Found. Trends® Mach. Learn. 2, 1–127 (2009). doi:10.1561/2200000006

    Article  MATH  Google Scholar 

  16. LeCun, Y.A., Bottou, L., Orr, G.B., Müller, K.-R.: Efficient BackProp. In: Orr, G.B., Müller, K.-R. (eds.) NIPS-WS 1996. LNCS, vol. 1524, pp. 9–50. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  17. Official website of H2O machine learning programming library. http://h2o.ai/. Accessed 8 August 2015

Download references

Acknowledgments

We kindly thank company NatuMed Sp. z o.o (Targowa 17a, 42-244 Wancerzow, Poland) for supplying us with SKL dataset that together with our own SKL recordings was used as training and validation dataset in this research.

Author information

Authors and Affiliations

  1. Institute of Computer Science and Computer Methods, Pedagogical University of Krakow, 2 Podchorazych Ave, 30-084, Krakow, Poland

    Tomasz Hachaj

  2. Cryptography and Cognitive Informatics Research Group, AGH University of Science and Technology, 30 Mickiewicza Ave, 30-059, Krakow, Poland

    Marek R. Ogiela

Authors
  1. Tomasz Hachaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marek R. Ogiela
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marek R. Ogiela .

Editor information

Editors and Affiliations

  1. College of Mathematics and Informatics, The South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. School of Computer Science, Guangzhou University, Guangzhou, China

    Jin Li

  3. School of Computer Science and Engineeri, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

  4. Dept. of Informatics, University of Salerno, Fisciano, Italy

    Aniello Castiglione

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media Singapore

About this paper

Cite this paper

Hachaj, T., Ogiela, M.R. (2016). Application of Neural Network for Human Actions Recognition. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_18

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-0356-1_18

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0355-4

  • Online ISBN: 978-981-10-0356-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation