Tensor Modification of Orthogonal Matching Pursuit Based Classifier in Human Activity Recognition

  • Pavel Dohnálek
  • Petr Gajdoš
  • Tomáš Peterek
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 210)


Human physical activity monitoring is a relatively new problem drawing much attention over the last years due to its wide application in medicine, homecare systems, prisoner monitoring etc. This paper presents Orthogonal Matching Pursuit as a method for activity recognition and proposes a new modification to the method that significantly increases the recognition accuracy. Both methods show promising results in both total recognition and differentiation between certain activities even without the necessity of prior data preprocessing. The methods were tested on raw sensor data.


orthogonalmatching pursuit activitymonitoring pattern recognition raw data 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Blumensath, T., Davies, M.E.: On the difference between orthogonal matching pursuit and orthogonal least squares (2007)Google Scholar
  2. 2.
    Lavanya, D., Rani, D.K.: Performance evaluation of decision tree classifiers on medical datasets. International Journal of Computer Applications 26(4), 1–4 (2011)CrossRefGoogle Scholar
  3. 3.
    Ermes, M., Parkka, J., Mantyjarvi, J., Korhonen, I.: Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. Trans. Info. Tech. Biomed. 12(1), 20–26 (2008) doi:10.1109/TITB.2007.899496,
  4. 4.
    Jang, J.S.R., Sun, C.T.: Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, Inc., Upper Saddle River (1997)Google Scholar
  5. 5.
    Jatoba, L.C., Grossmann, U., Kunze, C., Ottenbacher, J., Stork, W.: Context-aware mobile health monitoring: Evaluation of different pattern recognition methods for classification of physical activity. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2008, pp. 5250–5253 (2008), doi:10.1109/IEMBS.2008.4650398Google Scholar
  6. 6.
    Kelly, D., Caulfield, B.: An investigation into non-invasive physical activity recognition using smartphones. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3340–3343 (2012), doi:10.1109/EMBC.2012.6346680Google Scholar
  7. 7.
    Khan, A., Lee, Y.K., Lee, S., Kim, T.S.: A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. IEEE Transactions on Information Technology in Biomedicine 14(5), 1166–1172 (2010), doi:10.1109/TITB.2010.2051955CrossRefGoogle Scholar
  8. 8.
    Lin, S.W., Chen, S.C., Chen, S.C.: Parameter determination and feature selection for c4.5 algorithm using scatter search approach, pp. 63–75 (2012)Google Scholar
  9. 9.
    Loh, W.Y.: Classification and regression trees, pp. 14–23 (2011)Google Scholar
  10. 10.
    Naranjo-Hernandez, D., Roa, L., Reina-Tosina, J., Estudillo-Valderrama, M.: Som: A smart sensor for human activity monitoring and assisted healthy ageing. IEEE Transactions on Biomedical Engineering 59(11), 3177–3184 (2012), doi:10.1109/TBME.2012.2206384CrossRefGoogle Scholar
  11. 11.
    Pärkkä, J., Cluitmans, L., Ermes, M.: Personalization algorithm for real-time activity recognition using pda, wireless motion bands, and binary decision tree. Trans. Info. Tech. Biomed. 14(5), 1211–1215 (2010) doi:10.1109/TITB.2010.2055060,
  12. 12.
    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
  13. 13.
    Reiss, A., Stricker, D.: Introducing a new benchmarked dataset for activity monitoring. In: The 16th IEEE International Symposium on Wearable Computers, ISWC (2012)Google Scholar
  14. 14.
    Tapia, E., Intille, S., Haskell, W., Larson, K., Wright, J., King, A., Friedman, R.: Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In: 2007 11th IEEE International Symposium on Wearable Computers, pp. 37–40 (2007), doi:10.1109/ISWC.2007.4373774Google Scholar
  15. 15.
    Wei, B., Yang, M., Rana, R.K., Chou, C.T., Hu, W.: Distributed sparse approximation for frog sound classification. In: Proceedings of the 11th international conference on Information Processing in Sensor Networks, IPSN 2012, pp. 105–106. ACM, New York (2012) doi:10.1145/2185677.2185699,,
  16. 16.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 210–227 (2009), CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Pavel Dohnálek
    • 1
    • 2
  • Petr Gajdoš
    • 1
    • 2
  • Tomáš Peterek
    • 2
  1. 1.Department of Computer Science, FEECSVŠB - Technical University of OstravaOstrava-PorubaCzech Republic
  2. 2.IT4 Innovations, Centre of ExcellenceVŠB - Technical University of OstravaOstrava-PorubaCzech Republic

Personalised recommendations