Skip to main content
SpringerLink
Log in

Fast Human Activity Recognition Based on a Massively Parallel Implementation of Random Forest

  • Conference paper
Intelligent Information and Database Systems (ACIIDS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9622))

Included in the following conference series:

  • 1584 Accesses

Abstract

This article elaborates on the task of Human Activity Recognition being solved with the Random Forest algorithm. A performance measure is provided in terms of both recognition accuracy and computation speed. In addition, the Random Forest algorithm was implemented using CUDA, a technology providing options for massively parallel computations on low-cost hardware. The results suggest that Random Forest is a suitable and highly reliable technique for recognising human activities and that Graphics Processing Units can significantly improve the computation times of this otherwise rather time-consuming algorithm.

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. 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. Bańczyk, K., Kempa, O., Lasota, T., Trawiński, B.: Empirical comparison of bagging ensembles created using weak learners for a regression problem. In: Nguyen, N.T., Kim, C.-G., Janiak, A. (eds.) ACIIDS 2011, Part II. LNCS, vol. 6592, pp. 312–322. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  3. Ben-Arie, J., Wang, Z., Pandit, P., Rajaram, S.: Human activity recognition using multidimensional indexing. IEEE Trans. Pattern Anal. Mach. Intell. 24(8), 1091–1104 (2002)

    Article  Google Scholar 

  4. Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 257–267 (2001)

    Article  Google Scholar 

  5. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). http://dx.doi.org/10.1023/A%3A1010933404324

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

    Article  MathSciNet  MATH  Google Scholar 

  7. Cuzzocrea, A., Francis, S.L., Gaber, M.M.: An information-theoretic approach for setting the optimal number of decision trees in random forests. In: SMC, pp. 1013–1019 (2013)

    Google Scholar 

  8. Dabney, A.R.: Classification of microarrays to nearest centroids. Bioinformatics 21(22), 4148–4154 (2005)

    Article  Google Scholar 

  9. Denil, M., Matheson, D., de Freitas, N.: Narrowing the gap: Random forests in theory and in practice. CoRR abs/1310.1415 (2013)

    Google Scholar 

  10. Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  11. Freund, Y.: An adaptive version of the boost by majority algorithm. Mach. Learn. 43(3), 293–318 (2001). http://dx.doi.org/10.1023/A%3A1010852229904

  12. Gehrke, J.: Classification and regression trees. In: Encyclopedia of Data Warehousing and Mining, pp. 192–195 (2009)

    Google Scholar 

  13. Groupware@LES, R.G: Wearable computing: Classification of body postures and movements data set (2013). http://groupware.les.inf.puc-rio.br/har

  14. Hastie, T., Tibshirani, R.: Discriminant adaptive nearest neighbor classification. IEEE Trans. Pattern Anal. Mach. Intell. 18(6), 607–616 (1996)

    Article  Google Scholar 

  15. Kirk, D., Hwu, W.: Programming Massively Parallel Processors: A Hands-on Approach. Applications of GPU Computing Series, Elsevier Science (2010). http://books.google.cz/books?id=qW1mncii_6EC

  16. Kotsiantis, S.B.: Bagging and boosting variants for handling classifications problems: a survey. Knowl. Eng. Rev. 29(1), 78–100 (2014)

    Article  Google Scholar 

  17. Loh, W.Y.: Classification and regression trees. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 1(1), 14–23 (2011)

    Article  Google Scholar 

  18. nVIDIA: Nvidia kepler gk110 architecture whitepaper (2014). http://www.nvidia.com/content/PDF/kepler/NVIDIA-Kepler-GK110-Architecture-Whitepaper.pdf

  19. Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. Wadsworth International Group, California (1984)

    MATH  Google Scholar 

  20. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)

    Google Scholar 

  21. Reiss, A., Stricker, D.: Creating and benchmarking a new dataset for physical activity monitoring. In: The 5th Workshop on Affect and Behaviour Related Assistance (ABRA) (2012)

    Google Scholar 

  22. Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., Forster, K., Troster, G., Lukowicz, P., Bannach, D., Pirkl, G., Ferscha, A., Doppler, J., Holzmann, C., Kurz, M., Holl, G., Chavarriaga, R., Sagha, H., Bayati, H., Creatura, M., del R Millan, J.: Collecting complex activity datasets in highly rich networked sensor environments. In: 2010 Seventh International Conference on Networked Sensing Systems (INSS), pp. 233–240, June 2010

    Google Scholar 

  23. Sagha, H., Digumarti, S.T., del R Millan, J., Chavarriaga, R., Calatroni, A., Roggen, D., Troster, G.: Benchmarking classification techniques using the opportunity human activity dataset. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 36–40. IEEE (2011)

    Google Scholar 

  24. Ugulino, W., Cardador, D., Vega, K., Velloso, E., Milidiú, R., Fuks, H.: Wearable computing: accelerometers’ data classification of body postures and movements. In: Barros, L.N., Finger, M., Pozo, A.T., Gimenénez-Lugo, G.A., Castilho, M. (eds.) SBIA 2012. LNCS, vol. 7589, pp. 52–61. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  25. Wang, Y., Huang, K., Tan, T.: Human activity recognition based on r transform. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR 2007, pp. 1–8. IEEE (2007)

    Google Scholar 

Download references

Acknowledgement

This work was supported by the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070), funded by the European Regional Development Fund and the national budget of the Czech Republic via the Research and Development for Innovations Operational Programme and by Project SP2015/105 “DPDM - Database of Performance and Dependability Models” of the Student Grand System, VŠB Technical University of Ostrava and by Project SP2015/146 “Parallel processing of Big data 2” of the Student Grand System, VŠB Technical University of Ostrava.

Author information

Authors and Affiliations

  1. Department of Computer Science, FEECS, VŠB - Technical Univesity of Ostrava, 17. listopadu 15, 708 33, Ostrava, Czech Republic

    Jan Janoušek, Petr Gajdoš, Pavel Dohnálek & Michal Radecký

  2. IT4innovations, Centre of Excellence, VŠB - Technical Univesity of Ostrava, 17. listopadu 15, 708 33, Ostrava, Czech Republic

    Jan Janoušek, Petr Gajdoš & Pavel Dohnálek

Authors
  1. Jan Janoušek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Petr Gajdoš
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pavel Dohnálek
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michal Radecký
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jan Janoušek .

Editor information

Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Wrocław University of Technology, Wrocław, Poland

    Bogdan Trawiński

  3. Iwate Prefectural University, Takizawa, Japan

    Hamido Fujita

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Janoušek, J., Gajdoš, P., Dohnálek, P., Radecký, M. (2016). Fast Human Activity Recognition Based on a Massively Parallel Implementation of Random Forest. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49390-8_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-49390-8_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49389-2

  • Online ISBN: 978-3-662-49390-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation