Comparative Evaluation of Feature Extraction Methods for Human Motion Detection

Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 437)


In this article we conduct an evaluation of feature extraction methods for the problem of human motion detection based on 3-dimensional inertial sensor data. For the purpose of this study, different preprocessing methods are used, and statistical as well as physical features are extracted from the motion signals. At each step, state-of-the-art methods are applied, and the produced results are finally compared in order to evaluate the importance of the applied feature extraction and preprocessing combinations, for the human activity recognition task.


Accelerometers movement classification human motion recognition 


  1. 1.
    Jia, Y.: Diatetic and exercise therapy against diabetes mellitus. In: Second International Conference on Intelligent Networks and Intelligent Systems, pp. 693–696 (2009) Google Scholar
  2. 2.
    Mantyjarvi, J., Himberg, J., Seppanen, T.: Recognizing human motion with multiple acceleration sensors. In: IEEE Int. Conf. Syst., Man, Cybern., vol. 2, pp. 747–752 (2001)Google Scholar
  3. 3.
    Sekine, M., Tamura, T., Akay, M., Fujimoto, T., Togawa, T., Fukui, Y.: Discrimination of walking patterns using wavelet-based fractal analysis. IEEE Trans. Neural Syst. Rehabil. Eng. 10(3), 188–196 (2002)CrossRefGoogle Scholar
  4. 4.
    Ermes, M., Parkka, J., Mantyjarvi, J., Korhonen, I.: Frequent Bit Pattern Mining Over Tri-axial Accelerometer Data Streams for Recognizing Human Activities and Detecting Fall. Procedia Computer Science 19, 56–63 (2013)CrossRefGoogle Scholar
  5. 5.
    Zhang, M., Sawchuk, A.: A Feature Selection-Based Framework for Human Activity Recognition Using Wearable Multimodal Sensors. In: BodyNets 2011, University of Southern California, Los Angeles (2011)Google Scholar
  6. 6.
    Bernecker, T., Graf, F., Kriegel, H., Moennig, C.: Activity Recognition on 3D Accelerometer Data. Technical Report (2012)Google Scholar
  7. 7.
    Karantonis, D.M., Narayanan, M.R., Mathie, M., Lovell, N.H., Celler, B.G.: Implementation of a Real-Time Human Movement Classifier Using a Triaxial Accelerometer for Ambulatory Monitoring. IEEE on Information Technology in Biomedicine 10 (2006)Google Scholar
  8. 8.
    Khan, A., Lee, Y., Lee, S.Y., Kim, T.: Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer. IEEE transactions on information technology in biomedicine 14(5) (2010)Google Scholar
  9. 9.
    Zhang, M., Sawchuk, A.: USC-HAD: A Daily Activity Dataset for Ubiquitous Activity Recognition Using Wearable Sensors. In: UbiComp 2012, USA (2012)Google Scholar
  10. 10.
    Khan, A.M., Lee, Y.K., Lee, S.Y.: Accelerometer’s Position Free Human Activity Recognition Using A Hierarchical Recognition Model. In: IEEE HealthCom (2010)Google Scholar
  11. 11.
    Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., Havinga, P.: Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey, University of Twente, The Netherlands (2010)Google Scholar
  12. 12.
    Burges, C.: A tutorial on Support Vector Machines for Pattern Recognition. In: Data Mining and Knowledge Discovery, vol. 2(2), pp. 121–167. Kluwer Academic Publishers (1998)Google Scholar
  13. 13.
    Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K.: Improvements to Platt’s SMO Algorithm for SVM Classifier Design. Neural Computation 13(3), 637–649 (2001)CrossRefzbMATHGoogle Scholar
  14. 14.
    Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity Recognition from Accelerometer Data, Department of Computer Science, Rutgers University (2005)Google Scholar
  15. 15.
    Robnik-Sikonja, M., Kononenko, I.: An adaptation of Relief for attribute estimation in regression. In: ICML 1997, pp. 296–304 (1997)Google Scholar
  16. 16.
    Brezmes, T., Gorricho, J.-L., Cotrina, J.: Activity recognition from accelerometer data on a mobile phone. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009, Part II. LNCS, vol. 5518, pp. 796–799. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  1. 1.Multidimensional Data Analysis and Knowledge Management Laboratory, Dept. of Computer Engineering and InformaticsUniversity of PatrasRion-PatrasGreece

Personalised recommendations