Skip to main content
SpringerLink
Log in

A Case Study in R to Recognize Human Activity Using Smartphones

  • Conference paper
  • First Online:
Proceedings of International Conference on Computational Intelligence and Data Engineering

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 9))

Abstract

Physical activity recognition is a growing area of research with many applications in medical, surveillance systems and manufacturing industry. We perform a case study to classify the human activity into six categories—Standing, Walking, Walking_upstairs, Walking_downstairs, Sitting and Lying using Random Forest algorithm in R. The dataset is of high dimensional numeric data, this report focuses on two Preprocessing methods—Principal Component Analysis and Near zero variance with removal of correlated predictors to identify the best suitable data reduction techniques. This study highlights the Influence of preprocessing, the procedure to fine tune the model.

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

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Ronao, Charissa Ann, and Sung-Bae Cho. “Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models.” Natural Computation (ICNC), 2014 10th International Conference on. IEEE, 2014.

    Google Scholar 

  2. R. Poppe. Vision-based human motion analysis: An overview. Computer Vision and Image Under-standing, 108(1–2):4–18, 2007.

    Google Scholar 

  3. P. Lukowicz, J.A. Ward, H. Junker, M. Stager, G. Troster, A. Atrash, and T. Starner. Recognizing workshop activity using body worn microphones and accelerometers. Proceedings of the 2nd Int Conference Pervasive Computing, pages 18–22, 2004.

    Google Scholar 

  4. D.M. Karantonis, M.R. Narayanan, M. Mathie, N.H. Lovell, and B.G. Celler. Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Transactions on Information Technology in Biomedicine, 10(1):156–167, 2006.

    Google Scholar 

  5. R. Nishkam, D. Nikhil, M. Preetham and M.L. Littman. Activity recognition from accelerometer data. In Proceedings of the Seventeenth Conference on Innovative Applications of Artificial Intelligence,pages 1541–1546, 2005.

    Google Scholar 

  6. Yang, Rong, and Baowei Wang. “PACP: A Position-Independent Activity Recognition Method Using Smartphone Sensors.” Information 7, no. 4 (2016): 72.

    Google Scholar 

  7. Sunny, J.T., George, S.M., Kizhakkethottam, J.J., Sunny, J.T., George, S.M., & Kizhakkethottam, J.J. Applications and Challenges of Human Activity Recognition using Sensors in a Smart Environment. International Journal, 2, 50–57.

    Google Scholar 

  8. Frontiers of Human Activity Analysis. http://michaelryoo.com/cvpr2011tutorial/tutorial_cvpr2011_intro.pdf.

  9. Human Activity Recognition using SmartPhones Dataset. https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones.

  10. Human Activity Recognition using SmartPhones Dataset. http://rstudio-pubstatic.s3.amazonaws.com/24009_c068b79c74ae4fec8913fc0bf7a8b451.html.

  11. Physical Human Activity Recognition using Wearable Sensors. http://www.mdpi.com/1424-8220/15/12/29858/pdf.

  12. Anguita, Davide, et al. “Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine”.

    Google Scholar 

  13. Han, Jiawei, Jian Pei, and Micheline Kamber. Data mining: concepts and techniques. Elsevier, 2011.

    Google Scholar 

  14. Building Predictive Models in R using the Caret Package. http://web.ipac.caltech.edu/staff/fmasci/home/astro_refs/BuildingPredictiveModelsR_caret.pdf.

  15. Data PreProcessing. https://www.rdocumentation.org/packages/caret/versions/6.0-73/topics/preProcess.

  16. Arlot, Sylvain, and Alain Celisse. “A survey of cross-validation procedures for model selection.” Statistics surveys 4 (2010): 40–79.

    Google Scholar 

  17. Kuhn, Max. “Predictive Modeling with R and the caret Package”.

    Google Scholar 

  18. Data PreProcessing. https://topepo.github.io/caret/available-models.html.

  19. Visa, S., Ramsay, B., Ralescu, A.L. and Van Der Knaap, E., 2011, April. Confusion Matrix-based Feature Selection. In MAICS (pp. 120–127).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Vishnu Institute of Technology, Bhimavaram, India

    Kella Bhanu Jyothi & K. Hima Bindu

Authors
  1. Kella Bhanu Jyothi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Hima Bindu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kella Bhanu Jyothi .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Calcutta, Kolkata, West Bengal, India

    Nabendu Chaki

  2. DAIS, Ca’ Foscari University of Venice, Venice, Italy

    Agostino Cortesi

  3. Department of Information Technology, Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, India

    Nagaraju Devarakonda

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bhanu Jyothi, K., Hima Bindu, K. (2018). A Case Study in R to Recognize Human Activity Using Smartphones. In: Chaki, N., Cortesi, A., Devarakonda, N. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 9. Springer, Singapore. https://doi.org/10.1007/978-981-10-6319-0_17

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6319-0_17

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6318-3

  • Online ISBN: 978-981-10-6319-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation