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PCA Based Optimal ANN Classifiers for Human Activity Recognition Using Mobile Sensors Data

  • Kishor H. Walse
  • Rajiv V. Dharaskar
  • Vilas M. Thakare
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 50)

Abstract

Mobile Phone used not to be matter of luxury only, it has become a significant need for rapidly evolving fast track world. This paper proposes a spatial context recognition system in which certain types of human physical activities using accelerometer and gyroscope data generated by a mobile device focuses on reducing processing time. The benchmark Human Activity Recognition dataset is considered for this work is acquired from UCI Machine Learning Repository, which is available in public domain. Our experiment shows that Principal Component Analysis used for dimensionality reduction brings 70 principal components from 561 features of raw data while maintaining the most discriminative information. Multi Layer Perceptron Classifier was tested on principal components. We found that the Multi Layer Perceptron reaches an overall accuracy of 96.17 % with 70 principal components compared to 98.11 % with 561 features reducing time taken to build a model from 658.53 s to 128.00 s.

Keywords

Principal component analysis (PCA) Human activity recognition (HAR) Multi-layer perceptron (MLP) Smartphone Sensor Accelerometer Gyroscope 

References

  1. 1.
  2. 2.
    Acay, L.D.: Adaptive user interfaces in complex supervisory tasks. M.S. thesis, Oklahoma State University (2004)Google Scholar
  3. 3.
    Korpipaa.: Blackboard-based software framework and tool for mobile. Ph.D. thesis. University of Oulu, Finland (2005)Google Scholar
  4. 4.
    Zeng, M.: Convolutional neural networks for human activity recognition using mobile sensors. In: 6th International Conference on Mobile Computing, Applications and Services (MobiCASE) doi: 10.4108/icst.mobicase.2014.257786 (2014)
  5. 5.
    Rao, F., Song, Y., Zhao, W.: Human activity recognition with smartphonesGoogle Scholar
  6. 6.
    Yang, J.: Toward physical activity diary: motion recognition using simple acceleration features with mobile phones. In: ACM IMCE’09, Beijing, China, 23 Oct 2009Google Scholar
  7. 7.
    Jalal, A., Kamal, S., Kim, D.: Depth map-based human activity tracking and recognition using body joints features and self-organized map. In: 5th ICCCNT—2014, 2 Hefei, China, 11–13 Jul 2014Google Scholar
  8. 8.
    Dernbach, S., Das, B., Krishnan, N.C., Thomas, B.L., Cook, D.J.: Simple and complex activity recognition through smart phones. In: Intelligent Environments (IE) 8th International Conference, pp. 214–221 (2012). doi: 10.1109/IE.2012.39
  9. 9.
    Walse, K.H., Dharaskar, R.V., Thakare, V.M.: Frame work for adaptive mobile interface: an overview. In: IJCA Proceedings on National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2012) vol. 14, pp. 27–30 (2012)Google Scholar
  10. 10.
    Walse, K.H., Dharaskar, R.V., Thakare, V.M.: Study of framework for mobile interface. In: IJCA Proceedings on National Conference on Recent Trends in Computing NCRTC9, pp. 14–16 (2012)Google Scholar
  11. 11.
    Rizwan, A., Dharaskar, R.V.: Study of mobile botnets: an analysis from the perspective of efficient generalized forensics framework for mobile devices. In: IJCA Proceedings on National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2012) ncipet 15, pp. 5–8 (2012)Google Scholar
  12. 12.
    Anguita, D.: A public domain dataset for human activity recognition using smartphones. In: 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN-2013. Bruges, Belgium (2013)Google Scholar
  13. 13.
    Kharat, P.A., Dudul, S.V.: Daubechies wavelet neural network classifier for the diagnosis of epilepsy. Wseas Trans. Biol. Biomed. 9(4), 103–113 (2012)Google Scholar
  14. 14.
    Devenport, K.: Samsung phone data analysis project, 19 Mar 2013. Blog http://kldavenport.com/samsung-phone-data-analysis-project/
  15. 15.
    Ronao, C.A., Cho, S.-B.: Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models. In: 10th International Conference on Natural Computation (ICNC), pp. 681–686, 19–21 Aug 2014. doi: 10.1109/ICNC.2014.6975918

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Kishor H. Walse
    • 1
  • Rajiv V. Dharaskar
    • 2
  • Vilas M. Thakare
    • 3
  1. 1.Department of CSEAnuradha Engineering CollegeChikhliIndia
  2. 2.DMAT-Disha Technical CampusRaipurIndia
  3. 3.Department of CSS.G.B.A. UniversityAmravatiIndia

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