A Study on the Effect of Adaptive Boosting on Performance of Classifiers for Human Activity Recognition

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 469)


Nowadays, all smartphones are equipped with powerful multiple built-in sensors. People are carrying these “sensors” nearly all the time from morning to night before sleep as they carry the smartphone all the time. These smartphone allow the data to be collected through built-in sensors, especially the accelerometer and gyroscope give us several obvious advantages in the human activity recognition research as it allow the data to be collected anywhere and anytime. In this paper, we make use of publicly available dataset online and try to improve the classification accuracy by choosing the proper learning algorithm. The benchmark dataset considered for this work is acquired from the UCI Machine Learning Repository which is available in public domain. Our experiment indicates that combining AdaBoost.M1 algorithm with Random Forest, J.48 and Naive Bayes contributes to discriminating several common human activities improving the performance of Classifier. We found that using Adaboost.M1 with Random Forest, J.48 and Naive Bayes improves the overall accuracy. Particularly, Naive Bayes improves overall accuracy of 90.95 % with Adaboost.M1 from 79.89 % with simple Naive Bayes.


Human activity recognition (HAR) Smartphone Sensor Accelerometer Gyroscope 


  1. 1.
    Rao F., Song Y., Zhao W.: Human Activity Recognition with SmartphonesGoogle Scholar
  2. 2.
    Yang J.: Toward Physical Activity Diary: Motion Recognition Using Simple Acceleration Features with Mobile Phones, ACM IMCE’09, October 23; Beijing, China (2009)Google Scholar
  3. 3.
    Acay L. D.: Adaptive User Interfaces in Complex Supervisory Tasks, M.S.thesis, Oklahoma State Univ; (2004)Google Scholar
  4. 4.
    Korpipaa: Blackboard-based software framework and tool for mobile, Ph.D. thesis.University of Oulu, Finland (2005)Google Scholar
  5. 5.
    Ming Zeng: Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors, 6th Intl. Conf. on Mobile Computing, Applications and Services (MobiCASE) DOI: 10.4108/icst.mobicase.2014.257786 (2014)
  6. 6.
    Jalal A., Kamal S., Kim D.: Depth Map-based Human Activity Tracking and Recognition Using Body Joints Features and Self-Organized Map, 5th ICCCNT - 2014 July 11 – 13, 2 Hefei, China (2014)Google Scholar
  7. 7.
    Dernbach S., Das B., Krishnan N. C., Thomas B.L., Cook D.J. Simple and Complex Activity Recognition through Smart Phones. Intelligent Environments (IE) 8th International Conference 214–21. doi: 10.1109/IE.2012.39 (2012)
  8. 8.
    Walse K.H., Dharaskar R.V., Thakare V.M.: Frame work for Adaptive Mobile Interface: An Overview. IJCA Proceedings on National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2012)14; 2012:27–30 (2012)Google Scholar
  9. 9.
    Walse K.H., Dharaskar R.V., Thakare V.M.: Study of Framework for Mobile Interface. IJCA Proceedings on National Conference on Recent Trends in Computing NCRTC9; 14–16 (2012)Google Scholar
  10. 10.
    Rizwan A., Dharaskar R.V.: Study of mobile botnets: An analysis from the perspective of efficient generalized forensics framework for mobile devices. IJCA Proceedings on National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2012) ncipet 15; 2012: pp. 5–8 (2012)Google Scholar
  11. 11.
    Anguita D.: A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN-2013. Bruges, Belgium (2013)Google Scholar
  12. 12.
    Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. In Proceedings of the Second European Conference on Computational Learning Theory, 1995Google Scholar
  13. 13.
    A. Sharma, Y.-D. Lee, and W.-Y. Chung: High accuracy human activity monitoring using neural network,” International Conference on Convergence and Hybrid Information Technology, pp 430–435 (2008)Google Scholar
  14. 14.
    Ronao, C.A.; Sung-Bae Cho: Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models.Natural Computation (ICNC), 2014 10th International Conference on, vol., no., pp. 681–686, 19-21 Aug. 2014 doi: 10.1109/ICNC.2014.6975918
  15. 15.
    D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz: A public domain dataset for human activity recognition using smartphones,” European Symposium on Artificial Neural Networks (ESANN), pp. 437–442 (2013)Google Scholar
  16. 16.
    Y. Hanai, J. Nishimura, and T. Kuroda: Haar-like filtering for human activity recognition using 3d accelerometer. In Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009. DSP/SPE 2009. IEEE 13th,pages 675–678 (2009)Google Scholar
  17. 17.
    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

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  1. 1.S.G.B. Amravati UniversityAmravatiIndia
  2. 2.DMAT-Disha Technical CampusRaipurIndia
  3. 3.P.G. Department of CSS.G.B. Amravati UniversityAmravatiIndia

Personalised recommendations